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An analysis of cyanobacterial bloom occurrence in Missisquoi Bay (Québec, Canada) between 2000 and 2008, and possible environmental factors underlying them

Published by the NSW Department of Primary Industries, Office of Water

An analysis of cyanobacterial bloom occurrence in Missisquoi Bay (Québec, Canada) between 2000 and 2008, and

possible environmental factors underlying them

First published July 2014

ISBN 978 1 74256 652 8

More information

Lee Bowling, Office of Water, NSW Department of Primary Industries, Menangle, New South Wales, Australia

Sylvie Blais, Ministère du Développement durable, de l’Environnement, de la Faune et des Parcs, Québec City,

Québec, Canada

Marc Sinotte, Ministère du Développement durable, de l’Environnement, de la Faune et des Parcs, Québec City,

Québec, Canada

www.dpi.nsw.gov.au

© State of New South Wales through the Department of Trade and Investment, Regional Infrastructure and Services, 2014. You may copy, distribute

and otherwise freely deal with this publication for any purpose, provided that you attribute the NSW Department of Primary Industries as the owner.

Disclaimer: The information contained in this publication is based on knowledge and understanding at the time of writing (July 2014). However, because

of advances in knowledge, users are reminded of the need to ensure that information upon which they rely is up to date and to check currency of the

information with the appropriate officer of the Department of Primary Industries or the user’s independent adviser.

JT 12990

http://www.dpi.nsw.gov.au/

Analysis of cyanobacterial blooms in Missisquoi Bay (Québec, Canada), 2000 - 2008

Acknowledgments

This study was supported by a travel grant to Lee Bowling from the Australian Academy of Science under their

Scientific Visits to North America program. We thank the Ministère du Développement durable, de l’Environnement,

de la Faune et des Parcs (MDDEFP) of Québec, Canada, for hosting this visit. Office of Water, New South Wales

Department of Primary Industries, Australia, also supported this visit and the completion of the study.

The authors want to thank all the members of the MDDEFP production team (listed below) for contributing their

expertise to one or more of the following stages of this study:

Scientific editing Sylvie Blais1, Marc Sinotte

1, Martin Mimeault

2

Field sampling, measurement and

comments

Daniel Blanchard3, Patrick Chevrette

3, Mathieu Choinière

3, Guy

Desgranges3

, Martin Mimeault2, Sylvain Primeau

2, Richard Smith

3,

Alain Tanguay3, Sylvie Legendre

1,6, Yves Laporte

1,6

Cyanotoxin laboratory analysis Christian Deblois4 (senior analyst), Annie Laverdière

4, Ginette

Côté4, Mireille Brunet

4, Valérie Gauthier

4, Martin Duchesneau

4,

François Houde4, Nathalie Dassylva

4, Danielle Thomassin

4, Marie-

Claire Grenon4 and Benoît Sarrasin

4 as well as some part-time

employees4

Cyanobacteria and eukaryotic algae

(except in 2000), chlorophyll and

phycocyanin laboratory analysis

Christian Bastien4

(senior analyst), Éloïse Veilleux4, Richard

Cardin4, Karine Ménard-Cloutier

4, Nancy Desrosiers

4, Cathy

Tremblay4, Nicole Cadoret

4


laboratory analysis (2000)

Serge Paquet5

Laboratory inorganic chemistry

parameter analyses

Nathalie Dassylva4

, François Houde4, Danielle Thomassin

4,

Andrée Gendron4

(senior analysts), Gilles Labbé4, Jean-Guy

Samson4, Hélène Ferland

4, Alberte Lauzier

4, Louise Simoneau

4,

Diane Veillette4, Serge Morissette

4 and Steeve Roberge

4

Data capture and verification Nathalie Bourbonnais1, Yves Laporte

1, Sylvie Legendre

1,

Guillaume Desrosiers1, Marie-Ève Thériault

1

Maps Mona Frenette1

Computer graphics France Gauthier1

1. Direction du suivi de l’état de l’environnement (DSEE), Ministère du Développement durable, de l’Environnement,

de la Faune et des Parcs (MDDEFP)

2. Direction régionale de l’analyse et de l’expertise de l’Estrie et de la Montérégie, (DRAE), MDDEFP

3. Centre de contrôle environnemental du Québec de l'Estrie et de la Montérégie (CCEQ), MDDEFP

4. Centre d’expertise en analyse environnementale du Québec (CEAEQ) au MDDEFP.

5. Université du Québec à Montréal

6. DSEE participated in fieldwork only in the first annual study.

Special thanks go to Martin Mimeault, coordinator of Lake Champlain integrated management at the MDDEFP’s

Direction régionale de l’analyse et de l’expertise de l’Estrie et de la Montérégie (DRAE). In addition to providing the

data, he also was the advocate at DRAE for ensuring that it contributed regional technical resources for fieldwork and

laboratory time for sample analysis. He contributed also by reviewing the draft report and advising on nutrients

sources within Missisquoi Bay.

We also thank Mme France Gauthier of MDDEFP for the production of figures within the report.

We thank David Ryan of the NSW Office of Water for assistance with the statistical analyses.

Cover photo: Cyanobacteria bloom in Missisquoi Bay, north of the Saint-Armand public dock, August 26, 2008.

(Source: Patrick Chevrette, Centre de contrôle environnemental du Québec de l'Estrie et de la Montérégie, MDDEFP).


i NSW Office of Water, July 2014

Contents Executive summary ..................................................................................................................... v

1 Introduction .............................................................................................................................. 1

2 Methods ................................................................................................................................... 3

2.1 Field samping and data collection ...................................................................................... 3

2.1.1 Sampling sites and strategies ...................................................................................... 3

2.1.2 Data collection............................................................................................................. 4

2.2 Laboratory analysis............................................................................................................ 5

2.3 Classification of potentially toxic cyanobacteria ................................................................. 6

2.4 Statistical analysis ............................................................................................................. 6

3 Results ..................................................................................................................................... 8

3.1 Annual variation in cyanobacterial presence at sites a, b, d2 and d8 ................................. 8

3.2 Contribution of Cyanobacteria to the Total Phytoplankton Biomass ................................. 12

3.3 Interactions between phytoplankton and cyanobacterial taxa .......................................... 16

3.4 Cyanobacteria and toxin presence ................................................................................... 18

3.5 Eukaryotic and cyanobacterial presence and water quality indicators .............................. 25

3.5.1 Analysis of the combined data for sites a, b, d2 and d8 ............................................. 25

3.5.2 Site by site analysis ................................................................................................... 27

3.6 Meteorological factors and cyanobacterial and eukaryotic algal presence ....................... 28

3.7 Multivariate analysis of the cyanobacterial and environmental data ................................. 32

3.7.1 Preliminary analysis of the complete cyanobacterial data set .................................... 32

3.7.2 Analysis of the reduced cyanobacterial data set ........................................................ 33

3.7.3 Analysis of the environmental data set ...................................................................... 34

3.7.4 Comparison of the reduced cyanobacterial data set and the environmental data set 35

3.7.5 Comparisons on a site by site basis .......................................................................... 36

4 Discussion.............................................................................................................................. 36

5 Conclusions ........................................................................................................................... 43

6 References ............................................................................................................................. 45

Appendix A List of sampling sites in the Québec portion of Missisquoi Bay, their location, the

number of samples collected at each (between 2000 and 20081), and the period of record ...... 49

Appendix B Analytical methodology used by CEAEQ ................................................................ 51


ii NSW Office of Water, July 2014

Appendix C Cyanobacterial taxa that were present at sites a, b, d2 and d8 for the entire

sampling campaigns between 2000 and 2008, and their dominance ......................................... 52

Appendix D Spearman Rank Order Correlation coefficients (Rho) for interactions between

various cyanobacterial and eukaryotic algal groupings and taxa ............................................... 58

Appendix E Scatter diagrams of total microcystin concentrations against various water quality

attributes (sites a, b, d2 and d8 combined), and Spearman Rank Order Correlation coefficients

(Rho) ......................................................................................................................................... 62

Appendix F Spearman Rank Order Correlation coefficients (Rho) for interactions between

various cyanobacterial and eukaryotic algal groupings and taxa against physico-chemical

variables ................................................................................................................................... 66

Appendix G Spearman Rank Order Correlation coefficients (Rho) and probabilities (p) for

interactions between the biomass of various cyanobacterial and eukaryotic algal taxa (log(x)

transformed) and meteorological variables measured at Philipsburg manual weather station ... 75

Appendix H Output from the multivariate analyses .................................................................... 99

Figure 1 Location of sampling sites and main public usage areas in Missisquoi Bay. “Patrolled

public beach” indicates that at least one lifeguard is on duty. For this report, only data from sites

a, b, d2 and d8 were used ........................................................................................................... 4

Figure 2 Total cyanobacterial abundance, as cell numbers (A) and as biomass (B) at site a in the

Québec portion of Lake Champlain. Note the change in Y axis scale from Figures 3, 4 and 5 .... 9

Figure 3 Total cyanobacterial abundance, as cell numbers (A) and as biomass (B) at site b in the

Québec portion of Lake Champlain. Note the change in Y axis scale from Figures 2, 4 and 5 . 10

Figure 4 Total cyanobacterial abundance, as cell numbers (A) and as biomass (B) at site d2 in

the Québec portion of Lake Champlain. Note the change in Y axis scale from Figures 2, 3 and 5

.................................................................................................................................................. 11

Figure 5 Total cyanobacterial abundance, as cell numbers (A) and as biomass (B) at site d8 in

the Québec portion of Lake Champlain. Note the change in Y axis scale from Figures 2, 3 and 4

.................................................................................................................................................. 13

Figure 6 Contribution provided by various phytoplankton divisions (both cyanobacteria and

eukaryotes) to total phytoplankton biomass at site a, from 2000 to 2008 inclusive. Note the

difference in Y axis scale from Figures 7, 8 and 9 ..................................................................... 14


eukaryotes) to total phytoplankton biomass at site b, from 2000 to 2008 inclusive. Note the



eukaryotes) to total phytoplankton biomass at site d2, from 2000 to 2008 inclusive. Note the



eukaryotes) to total phytoplankton biomass at site d8, from 2000 to 2008 inclusive. Note the

difference in Y axis scale from Figures 6, 7 and 8. .................................................................... 16

Figure 10 The percentage of the total phytoplanktonic biomass contributed by cyanobacteria

(sites a, b, d2 and d8, combined) .............................................................................................. 17


iii NSW Office of Water, July 2014

Figure 11 Examples of some of the interactions between various species or groupings of

phytoplankton within Missisquoi Bay. Spearman Rank Order Correlation coefficients (Rho) (with

missing data excluded), number of observations (n) and probabilities (p) for each of the five

panels are as follows: Panel a - total eukaryotic phytoplankton biomass and total cyanobacterial

biomass (Rho = 0.01, n = 148, p = 0.86); Panel b – total Anabaena biomass and total

Microcystis biomass (Rho = 0.49, n = 87, p < 0.0001); Panel c – Anabaena flos-aquae biomass

and Aphanizomenon flos-aquae biomass (Rho = 0.40, n = 44, p = 0.007); Panel d – Anabaena

flos-aquae biomass and Microcystis sp. biomass (Rho = 0.33, n = 42, p = 0.03); and Panel e –

Aphanizomenon flos-aquae biomass and Microcystis sp. biomass (Rho = 0.75, n = 44, p <

0.0001). Note that for a significant number of samples there were missing data for one of the

two groupings or species in most panels. Using log (x + 1) transformation allows these to be

shown as 0 on either axis. ......................................................................................................... 18

Figure 12 Presence of microcystin-LR, microcystin-RR and microcystin-YR at site a. Note the

difference in Y axis scale from Figures 13, 14 and 15 ............................................................... 19

Figure 13 Presence of microcystin-LR, microcystin-RR and microcystin-YR at site b. Note the


Figure 14 Presence of microcystin-LR, microcystin-RR and microcystin-YR at site d2. Note the


Figure 15 Presence of microcystin-LR, microcystin-RR and microcystin-YR at site d8. Note the


Figure 16 Concentrations of microcystin-LR, RR and YR compared against each other, for sites

a, b, d2 and d8 on Missisquoi Bay combined, presented on a logarithmic scale. Data below the

level of detection have been removed. Spearman Rank Order Correlation coefficients (Rho),

number of observations (n) and and probabilities (p) for each of the three panels are as follows:

MC-LR and MC-RR, Rho = 0.70, n = 47, p <0.0001; MC-RR and MC-YR, Rho = 0.29, n = 44, p

= 0.06; MC-LR and MC-TR, Rho = 0.43, n = 47, p = 0.003. ...................................................... 22

Figure 17 Concentrations of microcystin-LR, RR and YR and the total of all three variants

compared against total Microcystis biomass for sites a, b, d2 and d8 in Missisquoi Bay

combined, presented on a log scale. Data below the level of detection have been removed.

Spearman Rank Order Correlation coefficients (Rho), number of observations (n) and and

probabilities (p) for each of the four panels are as follows: Total Microcystis biomass and total

MC, Rho = 0.64, n = 70, p <0.0001; Total Microcystis biomass and MC-LR, Rho = 0.62, n = 70, p

<0.0001; Total Microcystis biomass and MC-RR, Rho = 0.48, n = 43, p = 0.001; Total

Microcystis biomass and MC-YR, Rho = 0.48, n = 43, p = 0.001. .............................................. 24

Figure 18 Variations of concentrations of total microcystin, MC-LR, MC-RR and MC-YR at sites

a, b, d2 and d8 in Missisquoi Bay combined in relation to water temperature. Microcystin data

are presented on a log scale, and data below the level of detection have been removed.

Spearman Rank Order Correlation coefficients (Rho), number of observations (n) and and

probabilities (p) for each of the four panels are as follows: Total microcystin and surface water

temperature, Rho = 0.20, n = 82, p = 0.08; MC-LR and surface water temperature, Rho = 0.08, n

= 82, p = 0.47; MC-RR and surface water temperature, Rho = 0.43, n = 45, p = 0.003; MC-YR

and surface water temperature, Rho = 0.47, n = 45, p = 0.001. ................................................. 26

Figure 19. Examples of scatterplots where the strongest correlations between cyanobacterial

biomass at sites a, b, d2 and d8 combined and meteorological conditions were usually obtained,

except wind speed (panel f). Note that in most panels there are a number of samples where

there is missing cyanobacterial biomass data. Using log (x + 1) transformation allows these to

be shown as 0 on the Y axis. Spearman Rank Order Correlation coefficients (Rho), number of


iv NSW Office of Water, July 2014

observations (n) and probabilities (p) for each of the six panels are as follows: Total Anabaena

biomass and antecedent 10 day maximum temperature, Rho = 0.22, n = 94, p = 0.03; A

spiroides biomass and antecedent 10 day minimum temperature, Rho = 0.36, n = 38, p = 0.02;

Total cyanobacterial biomass and antecedent 10 day rainfall, Rho = -0.26, n = 132, p = 0.003;

Total cyanobacterial biomass and antecedent 10 day cloud cover, Rho = -0.25, n = 132, p =

0.004; M. aeruginosa biomass and antecedent 10 day wind direction, Rho = -0.62, n = 22, p =

0.002; M. aeruginosa biomass and antecedent 10 day wind velocity, Rho = -0.14, n = 22, p =

0.52. .......................................................................................................................................... 30

Figure 20 Total cyanobacterial presence for all four Missiquoi Bay sites combined (blue

diamonds) compared against various meteorological variables from Philipsburg manual weather

station (histogram bars) ............................................................................................................ 31

Table 1 Spearman Rank Order Correlation coefficients (Rho) for variants of microcystins and for

the biomass of Microcystis spp, Anabaena spp. and Aphanizomenon flos-aquae for the

combined data collected from sites a, b, d2 and d8 in the Québec portion of Missisquoi Bay.

The data were log transformed with values below the level of detection excluded prior to the

analysis. Note: No attempt was done to adjust for multiple statistical significance testing,

therefore some correlations presented below might be declared significant by chance alone. .. 22

Table 2 90th percentile, median (in bold) and 10th percentile values for various water quality

variables at different sites on Missisquoi Bay. Number of observations (n) in brackets ............ 28


v NSW Office of Water, July 2014

Executive summary

Cyanobacterial (blue-green algal) and water quality data has been collected for the

northern (Québec, Canada) portion of Missisquoi Bay by the Ministère du

Développement durable, de l’Environnement, de la Faune et des Parcs (MDDEFP). The

sampling protocol was designed primarily to check for compliance against local

guidelines, mainly for recreational water use on the sampling dates when major blooms

or scums occurred. Samples were also collected to document biological, physical and

chemical conditions within the bay. This report focuses on the biological data and how

environmental factors may be driving cyanobacterial blooms within the bay. Compliance

with management guidelines are reported elsewhere (Blais, 2014).

Data were collected at four sites, including two along the eastern shoreline near Saint-

Armand (sites b, d2), one near the centre of the bay at the point of maximum depth (site

a) and one in the western part of the bay, near Venise-en-Québec (site d8). Sampling

occurred each summer period from 2000 to 2008, however not all sites were sampled

every year or at the same frequency each year, with greatest data collection occurring in

2001 and 2002. There were also slight changes in sampling method over the duration of

the monitoring.

Despite these temporal and spatial variations in sampling effort, the results highlight a

very dynamic and variable cyanobacterial bloom occurrence within Missisquoi Bay.

Generally sites a and d8 had lower cyanobacterial abundance than the two eastern

sites, but cyanobacterial blooms were particularly abundant at most sites from 2001 to

2006 and in 2008. In contrast, 2007 was a low bloom year. Microcystis was the

dominant genus present at most sites between 2001 and 2005. Blooms in subsequent

years have been more mixed, with dominance by potentially toxic species such as

Aphanizomenon flos-aquae, Anabaena flos-aquae and Anabaena spiroides at certain

times. Non-toxic species including Aphanothece clathrata and Chroococcus dispersus

were also sometimes dominant.

Cyanobacterial blooms in Missisquoi Bay are generally preceded by spring blooms of

diatoms and cryptophytes, but exponential growth of cyanobacteria in summer means

that these often come to contribute greater than 80% of the phytoplanktonic biomass at

that time of year. The cyanobacterial blooms decline in autumn, and are replaced again

by diatoms and cryptophytes, although generally at a lower biomass. In 2007 when

cyanobacterial blooms did not develop, green algae (Chlorophyceae) and dinoflagellates

(Peridinium) were dominant components of the phytoplankton.

The cyanotoxin microcystin was recorded at most sites in most years, apart from in

2007. Microcystin-LR was the most common variant present, but microcystin-YR and

microcystin-RR were also frequently present. The microcystin concentrations were

significantly positively correlated with Microcystis biomass but were rarely present when

other cyanobacterial genera were dominant. Microcystin-YR and microcystin-RR were

significantly positively correlated with surface water temperature, although microcystin-

LR and total microcystin were not; even if this relationship is not very strong it might

suggest that some taxa or genetic strains producing these microcystin variants are more

frequently present in warmer waters. Other factors that may have affected microcystin

concentrations include nutrients, pH and turbidity. Other cyanotoxins were usually below

detectable concentrations.


vi NSW Office of Water, July 2014

Multivariate analysis revealed that water quality varies considerably both spatially and

temporally. In particular the eastern sites tended to have higher total phosphorus

concentrations, turbidity and pH, and lower Secchi disc depths. The spatial variability in

water quality across the bay may be one factor leading to the variable composition of the

cyanobacterial communities. The bay is eutrophic, and a significant positive correlation

was found between cyanobacterial biomass and total phosphorus concentrations. In

comparison, total nitrogen concentrations were low and were only weakly correlated with

total cyanobacterial biomass. However oxidised nitrogen (NOx) was often significantly

negatively correlated with the biomass of various cyanobacterial taxa, indicating possible

uptake of this nutrient by the cyanobacteria. Cyanobacteria also frequently occurred at

TN:TP ratios of less than 7, indicating possible nitrogen limitation regardless of whether

heterocystous species were dominant or not. Significant positive correlations were also

shown between cyanobacterial biomass and pH and turbidity, and a significant negative

correlation occurred with Secchi disc depth. A number of taxa, especially of Anabaena,

were significantly positively correlated with dissolved organic carbon and several species

with water temperature.

Few significant correlations occurred between cyanobacterial biomass and the

meteorological conditions measured at the Philipsburg weather station, apart from the 7

and 10 day mean antecedent maximum air temperature, indicating that a week or longer

of hotter weather may benefit cyanobacteria in Missisquoi Bay in summer. Wind

conditions, periods of rainfall and prolonged cloud cover showed little effect.

Multivariate analysis also demonstrated the considerable temporal and spatial

heterogeneity in cyanobacterial bloom distribution and community composition in

Missisquoi Bay. Multivariate comparison of the cyanobacterial and environmental data

suggested that total nitrogen concentrations, pH and surface water temperature were the

environmental attributes most likely affecting cyanobacterial composition, but these

relationships were weak and there was a lot of unexplained variance in these data.

Correlation analysis indicated that chlorophyll-a and phycocyanin may both be good

indicators of cyanobacterial presence in Missisquoi Bay. There were few significant

correlations between eukaryotic algal biomass and the environmental attributes apart

from pH and surface water temperature.

The large amounts of unexplained variance suggest other unmeasured factors may be

causing major perturbations in the cyanobacterial communities of Missisquoi Bay.

Factors such as herbicide concentrations, the presence of zebra mussels and also of a

zooplanktivorous fish, the alewife, have been suggested by other authors. Unfortunately

there are no data to investigate these potential drivers of cyanobacterial community

composition and abundance in Missisquoi Bay.


1 NSW Office of Water, July 2014

1 Introduction

Missisquoi Bay comprises the north-eastern end of Lake Champlain. It is a shallow

(mean depth 2.8 metres) embayment, with a surface area of 77.5 km2 (McQuaid et al.,

2011; Wheeler et al., 2011). The international boundary between Canada and the

United States of America divides the bay in two, with the northern half being part of the

Canadian Province of Québec, and the southern half part of the State of Vermont in the

USA.

Problems of eutrophication were first documented for the Québec portion of Missisquoi

Bay during the late 1960s (Fontaine et al., 1968). Cyanobacterial blooms were first

recorded in the bay in the early 1980s (Bérubé, 1981; Germain et al., 1987). However

cyanobacterial blooms have become a prominent feature of Missisquoi Bay since 2000

(Watzin et al., 2006; Boyer, 2008; Zananski et al., 2010; Wheeler et al., 2012). Although

no single factor has been identified as the primary cause of cyanobacterial blooms in

Missisquoi Bay (Zananski et al., 2010), the bay is eutrophic, suffering excess

phosphorus inputs from agricultural and urban runoff (Wheeler et al., 2012). Medalie et

al. (2012) have shown that the Pike and Missisquoi Rivers which both discharge into

Missisquoi Bay provide high annual phosphorus and nitrogen loads, although annual

variation is considerable. Total phosphorus concentrations increased by 72% between

1979 and 2009 (Smeltzer et al., 2012), with concentrations averaging 45 μg l-1 between

1999 and 2003 (Zananski et al., 2010). McCarthy et al. (2013) have also found that

water column nitrogen regeneration processes play a primary role in supporting

productivity in Missisquoi Bay during the growing season, although this and external

nitrogen inputs from tributaries combined were still insufficient to fully support nitrogen

demand in the bay.

The Province of Québec has undertaken regular monitoring of cyanobacterial presence

in its half of Missisquoi Bay each summer since 2000. Between 2000 to 2008, public

health advisories due to cyanobacteria were issued for the bay each summer except for

2000 (Brault, 2012), with microcystin concentrations in scum regularly exceeding

Québec’s guidelines, therefore triggering partial or total closures of the bay for

recreational uses (including beach closures). Occasionally no-drinking advisories were

also issued for the drinking water treatment plant in Saint-Armand.

A number of previous studies have reported on cyanobacteria in Missisquoi Bay.

Smeltzer et al. (2012) compared phytoplankton data collected for mainly Vermont

monitoring programs on Missisquoi Bay for 1970-74, 1991-92, and 2006-09 and found a

shift in dominance from diatoms in the early 1970s and 1990s to cyanobacteria by 2006-

09. Palaeolimnological studies (Levine et al., 2012) using cores from the Québec part of

Missisquoi Bay have indicated that eutrophication of the bay began during the 1970s

when agricultural activities, including dairying and corn production intensified in the

catchment, with cyanobacterial blooms commencing as a result in the 1990s. Using

data from a New York monitoring program, Mihac et al. (2005) reported a spring

dominance of diatoms in 2002 in Lake Champlain, with a late summer shift to Microcystis

being most notable in the shallower northern parts of the lake, including Missisquoi Bay.

These authors also considered that there had been a recent change in cyanobacterial

dominance from Anabaena to Microcystis in northern parts of the lake. Watzin et al.

(2006), while reporting on cyanobacteria in Lake Champlain as a whole between 2002

and 2004 inclusive, reported that the southern (Vermont) portion of Missisquoi Bay had

the most consistent and severe cyanobacterial problems compared with other American



parts of the lake, exceeding World Health Organisation Alert Level 1 guidelines (Bartram

et al., 1999) each year. These blooms were dominated by large Microcystis colonies,

with the hepatotoxin microcystin also being detected. Mihac et al. (2006) reported that

the Missisquoi Bay phytoplankton community during the 2004 summer was comprised

primarily of cyanobacteria, with Microcystis as the dominant taxon but with a large

proportion of Aphanizomenon also present. Mihac et al. (2008) also mapped

cyanobacterial distributions throughout the whole of Missisquoi Bay during three cruises

conducted during the summer of 2006, which illustrated a complex spatial distribution of

blooms within the bay. They found extensive spatial variability in the size, extent and

location of cyanobacterial blooms around the bay, and that different taxa were dominant

at different locations. Finally Wheeler et al. (2012) have used satellite data to map the

distribution of cyanobacteria across the entire Missisquoi Bay in 2004, finding marked

spatial and temporal differences in its distribution.

Fortin et al. (2010) used a quantitative Polymerase Chain Reaction (qPCR) method to

track cyanobacterial blooms at Saint-Armand (Philipsburg sector) in the north-east of the

bay in 2006 and 2007. A bloom throughout summer and early autumn in 2006 was

numerically dominated by Microcystis aeruginosa, with Anabaena species occurring later

in autumn. Cyanobacterial presence at the site was much reduced in 2007, with some

Anabaena spiroides appearing in September. They concluded that Microcystis was the

principal microcystin producing genus in Missisquoi Bay during this study. Microcystis

was also the dominant cyanobacterium at a number of locations in the northern portion

of the bay in 2009, with toxigenic strains comprising 2 to 23% of the total Microcystis

population (Ngwa et al., 2014).

McQuaid et al. (2011) also reported on cyanobacterial presence in the north-east of

Missisquoi Bay close to the inlet of a water treatment plant in 2007 and 2008, reporting

low total cyanobacterial biovolume present in 2007, although high cell counts of the tiny

picoplanktonic species Aphanothece minutissima were encountered. In 2008 however

cyanobacteria once again dominated the phytoplankton community at this site, with both

Microcystis sp. and Anabaena sp. dominating. Zamyadi et al. (2012) further studied the

amount of cyanobacteria entering this drinking water treatment plant in untreated water

drawn from Missisquoi Bay, and reported that Microcystis aeruginosa dominated in the

inflow water in 2008 and 2009. In 2010 they found lower cyanobacterial concentrations

but high concentrations of cyanotoxins, especially microcystin-LR but also some

cylindrospermopsin.

Davis et al. (2009) included sites in Missisquoi Bay (in the US but also sometimes in

Québec) in their study of toxic and non-toxic strains of Microcystis undertaken in 2006.

The bay was devoid of cyanobacteria until July and then never numerically dominated by

Microcystis, but by Anabaena instead. Nutrients at the time were elevated. Toxic

Microcystis cells, determined by PCR, comprised approximately 6% of the total

Microcystis cells present. Pearce et al. (2013) utilised a self-organising map (SOM), a

non-linear and non-parametric clustering method, to investigate associations between

cyanobacterial blooms in Missisquoi Bay and in-lake environmental conditions from 2007

to 2009. They found the method separated the data into bloom and non-bloom periods

based on similarities in water and sediment chemistry and non-cyanobacterial

phytoplankton abundance. They also found that the dissolved nitrogen to soluble

reactive phosphorus ratios had a strong influence on determining Microcystis presence,

which was generally favoured by low ratios. However other undetermined environmental

factors may trigger blooms. Zananski et al. (2010) studied the effects of the herbicide

atrazine on cyanobacteria in water samples collected from the south-west (Vermont)



corner of the bay during the 2005 summer. Eukaryotic phytoplankton, in particular

Heterokontophyceae and Dinophyceae, were consistently major contributors to

chlorophyll-a, with cyanobacteria only dominating in June. These authors found some

seasonal variation in the responses of different divisions of phytoplankton to atrazine,

but were unable to conclude if any were more resistant to the herbicide than others.

Atrazine may have acted as a positive selective factor for cyanobacteria in one month of

their experiments, but at concentrations an order of magnitude higher than

concentrations found in Missisquoi Bay.

Most of the past studies of the cyanobacteria in Missisquoi Bay have been of short

duration (1 to 3 years), of limited spatial coverage, or have been part of broader studies

covering the entire Lake Champlain. The objective of this report is to provide an

integrated analysis of the blooms in the northern (Québec) portion of Missisquoi Bay

from 2000 to 2008 using data from the Québec monitoring program. Compliance with

management guidelines are reported elsewhere (Blais, 2014).

2 Methods

2.1 Field samping and data collection

2.1.1 Sampling sites and strategies

The largest cyanobacterial data sets for the Québec portion of Missisquoi Bay over the

longest periods of record are for sites a, b, d2 and d8. Data analysis for this report has

therefore concentrated on these 4 sites. Their locations are shown in Figure 1. A brief

description of all sites and the number of samples taken at each between 2000 and 2008

is given in Appendix A. The data used in this report are all for samples collected during

both regular sampling and bloom sampling occasions at these four sites between June

and October (summer and autumn in Québec, mostly for the years 2000 to 2008

inclusive). Some data from 2009 was also included in analyses that did not require inter-

year comparisons, but was otherwise not used elsewhere as 2009 data were only

available from late August to mid October and excluded the worst blooms of that

summer.

Site a is located in the deepest sector of the bay and at its centre. The justification for

sites b and d8 is firstly that they are located in important recreational use areas where

major body contact with the water occurs and secondly that there have been frequent

major blooms in these areas in recent years. Site b is also located facing the dominant

winds. Whenever the water level was too low, site b would be relocated further offshore

and as a consequence is considered a sector rather than a specific location. Site d2 is

frequently monitored because it is close to the intake point for the drinking water

treatment plant at Saint-Armand.

From one bloom study to another, site locations either remained unchanged or new ones

were set up. The goal was sampling at accessible locations where blooms and scum

seemed to be the most intense in both the eastern and western (i.e. Baie de Venise)

parts of Missisquoi Bay (access permission obtained from owners of private lands

however decreased over time). If permanent sites a or b were part of a major bloom

sector, they would also be used in the bloom studies. Otherwise, new “d” sites would be

set up or existing “d” sites would be resampled.



Figure 1 Location of sampling sites and main public usage areas in Missisquoi Bay. “Patrolled public beach” indicates that at least one lifeguard is on duty. For this report, only data from sites a, b, d2 and d8 were used

2.1.2 Data collection

The cyanobacterial monitoring reported in this report originated from two types of data

collection:

Regular studies that aim at providing a picture of the situation prior to visible blooms.

Generally speaking, these studies take place two or three times a year, at the

beginning of the season, with the last samples generally collected around mid-July.

For 2000, only regular studies took place, since no blooms were observed during that

year.

Bloom studies deal with situations where blooms are present, but representative

water samples were collected rather than concentrated surface scum samples.

Regular studies include the following laboratory-analyzed attributes: cyanobacteria,

eukaryotic algae, pigments such as chlorophyll-a and physicochemical attributes such as

phosphorus and nitrogen. During regular studies because of the absent of any blooms,

cyanotoxins are not usually measured, given the very low probability of detection and/or

measurement in appreciable concentrations. Bloom studies included all the above

attributes plus cyanotoxins.

Regular and bloom studies involved the collection of integrated samples from the

offshore water column from a boat. From 2000 to 2007 inclusive, integrated water



column sampling extended throughout the depth of the photic zone, but in 2008 and

2009 the sampling was restricted to the surface 1 metre (0-1 m). Between 2000 and

2003, samples were collected using a vertical Kemmerer sampling bottle. In 2000,

photic zone sampling was discontinuous, with sub-samples taken at 2-metre intervals,

starting at 30 cm below the water surface. From 2001 to 2003, the sub-samples

incorporated the entire depth of the photic zone on a continuous basis, starting from the

surface. From 2004 to 2007, the depth of the photic zone was sampled a single time

using a flexible plastic tube that was sufficiently long enough to extend through the entire

depth of the water column, independent of wherever the sampling occurred. In 2008 and

2009, 0-1 m samples were collected using a rigid PVC tube. The change from photic

zone to 0-1 m sampling was justified by the fact that the top metre of water is the depth

most frequently used during recreational activities that include water contact.

The depth of the photic zone was calculated by multiplying water transparency

measured with a Secchi disc by a factor of 2.7. This value is based on Cole (1975) and

Holdren et al. (2001), who recommend multiplying Secchi disk transparency by factors of

2.7 to 3 or 2 to 3 respectively. However, when the calculated photic zone reached or

exceeded the water column depth, the entire depth of the water column was sampled

apart from the bottom 30 to 50 cm, the goal being to avoid collection of water

contaminated with bottom sediments. Samples were well mixed before being divided into

attrribute-specific bottles that were subsequently analyzed in the laboratory. Surface

water temperature was measured by thermometer.

Over the entire monitoring period, sampling depth and instrumentation differences (three

different types of instruments were used) may have led to some disparity in results.

However, since the bay is quite large but not very deep and is highly wind-sensitive, as

such the water column is often stirred and is quite probably relatively homogenous.

Moreover, the number of studies and sampling sites per study varied from one year to

the next according to the available human and funding resources.

Meteorological data were obtained for the Philipsburg manual weather station for 2002

to 2008 inclusive. There were two records per day – one for 08:00 and one for 18:00

Québec local time. These were averaged to provide a single daily reading for the days

when phytoplankton samples had been collected. The data for the antecedent 4 days, 7

days and 10 days prior to phytoplankton sample collection were also averaged to obtain

a mean value for each of these antecedent periods. Some data within these antecedent

periods was sometimes missing and only the data available could be used to obtain the

mean value for that period.

2.2 Laboratory analysis

Water samples were analyzed by the MDDEFP’s Centre d’expertise en analyse

environnementale du Québec (CEAEQ). The methods used have been tabulated in

Appendix B. The 2000 cyanobacteria and eukaryotic algae samples were analyzed by

Université du Québec à Montréal (UQAM) which had developed the methodology earlier.

For the subsequent years, cyanobacteria and eukaryotic algae samples were analyzed

by CEAEQ using the expertise obtained by UQAM. Microcystin (MC) variants analyzed

during the entire monitoring period were limited to microcystin-LR (MC-LR), microcystin-

RR (MC-RR) and microcystin-YR (MC-YR).



2.3 Classification of potentially toxic cyanobacteria

These cyanobacteria are those likely to produce hepatotoxins such as microcystin (MC)

or cylindrospermopsin or neurotoxins such as anatoxin-a, saxitoxin or neosaxitoxin, etc.

Cyanobacterial species or types that according to the scientific literature (reviewed by

Blais and Sinotte, 2008) are known to produce at least these cyanotoxins in various

parts of the world were listed to define those potentially toxic species. Using a preventive

approach, we considered that these cyanobacteria are also likely to produce similar

toxins in Québec water bodies.

The taxonomy of a number of cyanobacterial species has been revised recently,

resulting in name changes to some species. In particular, gas-vacuolate planktonic

species from the genus Anabaena have now been assigned to a revised genus,

Dolichospermum (Waklin et al., 2009). A number of these species occur commonly in

Missisquoi Bay, including Anabaena flos-aquae (now Dolichospermum flos-aquae), A.

planctonica (now D. planctonicum), A. spiroides (now D. spiroides) and A. crassa (or A.

spiroides var crassa) (now D. crassum). In addition, Aphanothece clathrata has been

renamed Anathece clathrata (Komárek et al., 2011). However for consistency with

previous reporting from Missisquoi Bay, the previous taxonomic names have been

retained for this report.

2.4 Statistical analysis

The data number (n) often differs between various phytoplankton and water quality

attributes, as not all attributes were analysed for every sampling occasion. This missing

data explains why n often differs from one set of data analyses to another, especially

with the correlation analyses.

Initial exploratory analysis to visualise the data as histograms or scatter plots was

undertaken using Microsoft Excel 2007. STATISTICA v9 was used to undertake

Spearman Rank Order Correlation (Rho – the symbol for Rho, ρ, has not been used in

this report to avoid confusion with p, for probability) analysis between individual

cyanobacterial, eukaryotic algal, toxin and environmental variables. Spearman Rank

Order Correlation, a nonparametric correlation method, was chosen because the data

were not normally distributed, having a large number of low observations for many taxa

of cyanobacteria within the data set and occasional very high values; these would

otherwise lead to the violation of parametric assumptions. This correlation method also

does not simply calculate a linear correlation, but any correlation shape. Where taxa

were not observed in a sample, these were not assigned a zero value for that sample,

but excluded from the analysis. The correlation analyses were undertaken both on the

combined data from sites a, b, d2 and d8, and separately for each site after logarithmic

(log(x)) transformation of the cyanobacterial data and other environmental data where

appropriate, to account for the occasional high values that were skewing the

distributions.

In contrast, the data used for the scatter plots and other figures included zeros, and was

log (x + 1) transformed to avoid negative logarithmic values and to allow zero values to

be shown on either axis.

Despite the multiple comparisons that were made using correlation analysis, all results

having a level of significance of ≤ 0.05 are presented in this report. The exploratory

nature of this study justifies the absence of adjustment for multiple statistical significance

tests. We therefore acknowledge that type I error (i.e. probability of accepting a



correlation coefficient as different from zero when it is not) could be much higher than

5%. On the other hand applying any correction method, such as Bonferroni adjustment,

would in turn increase type II error (i.e. probability of rejecting a correlation coefficient as

non significant when it is actually different from zero), thereby reducing the power of the

test. The reader must keep in mind that some of the correlations presented might have

been declared statistically significant due to chance alone.

Multivariate analysis of the combined data for sites a, b, d2 and d8 was undertaken

using PRIMER v6 (Clarke and Gorley, 2006) and PERMANOVA+ (Anderson et al.,

2008). Separate site-specific analyses were also undertaken for sites a, b and d8, but

not for site d2 as there were insufficient data for this location. Rare species and those

with very low abundance (contributing very little to the total cyanobacterial biomass)

were either excluded from the cyanobacterial data set or rolled up to a genus level of

taxonomy, as appropriate. Samples where no cyanobacteria had been detected were

also excluded. Bray-Curtis similarity coefficients were then calculated using logarithmic

transformed (x + 1) cyanobacterial biomass data which were then used for non-metric

multidimensional scaling (nMDS) ordination and “Similarity, percentages” analysis

(SIMPER) to identify differences between sites, months and years and which

cyanobacterial taxa were contributing to this. The differences in community composition

between sampling sites, months and years were also tested using the Permutational

Multivariate Analysis of Variance (PERMANOVA) technique in the PERMANOVA+

program.

Water quality data from the 4 sites collected over the 2001 to 2008 sampling periods

were combined into an environmental data set containing total phosphorus, total

nitrogen, TN:TP (by concentration), pH, turbidity, surface water temperature, and Secchi

disc depth. Because some water quality attributes were not measured on all sampling

occasions, samples with missing data had to be deleted from the data set prior to

multivariate analysis. Meteorological data from Philipsburg manual weather station

(7026040) was not included as the same data would have been used for all 4 lake

sampling sites (i.e. there should be no differences between sites based on

meteorological data).

The environmental data set was normalised and then subjected to Principal Components

Analysis (PCA) and SIMPER to examine any variation between sites, years and months

using PRIMER v6 (Clarke and Gorley, 2006). The normalised data was also used to

create a resemblance matrix based on Euclidean distance, and PERMANOVA then used

to test for significant differences in the multivariate environmental data between sites,

years and months.

Because the environmental data set contained fewer samples than the cyanobacterial

data base, a second cyanobacterial data set of reduced size was created using only

samples matching those in the environmental data base. This reduced cyanobacterial

data set was then subjected to the same analyses as the original larger data set, so that

these new analytical outcomes could be directly compared with those of the PCA

analyses of the environmental data. This was undertaken to determine which of the

measured environmental attributes, if any, may be driving cyanobacterial community

composition and abundance in Missisquoi Bay. This was done in PRIMER v6 using

RELATE, an analysis which provides a measure of any overall correlation between the

two data sets. RELATE is also a rank correlation test similar to the Spearman Rank

Order Correlation test used previously, thereby providing analytical consistency over all

analyses. When RELATE showed a significant correlation between the environmental



and cyanobacterial data, BEST was used to determine which subset of environmental

variables was most strongly correlated with cyanobacterial community composition.

The reduced cyanobacterial data set and the environmental data set were then split into

data sets for sampling sites a, b and d8, and the 7 day average antecedent

meteorological data for Philipsburg added to each site-specific environmental data set.

There were insufficient data for site d2. Preliminary analysis indicated that surface water

temperature, minimum air temperature and maximum air temperature were all strongly

inter-corelated, containing effectively the same information and therefore 2 of these 3

variables were redundant for the purposes of the multivariate analysis. Likewise cloud

cover and rainfall were also strongly inter-correlated. Because of this, minimum air

temperature, maximum air temperature and rainfall were excluded from the

environmental data sets for further analysis. PCA, PERMANOVA and SIMPER analyses

were then performed on the environmental data for each site, and nMDS, PERMANOVA

and SIMPER on the cyanobacterial data for each site. RELATE and BEST were then

used to determine if there were any site-specific environmental drivers of the

cyanobacterial communities at each site.

3 Results

3.1 Annual variation in cyanobacterial presence at sites a, b, d2 and d8

Total cyanobacterial abundance at site a, as cell density (cells ml-1), is shown in Figure

2a, while abundance in terms of biomass is shown in Figure 2b. A summary of all

species occurring each year and their contributions according to biomass is provided in

Appendix C. Cyanobacterial abundance never exceeded 200,400 cells ml-1 (or 11,500

mg m-3 of biomass). Blooms in excess of 50,000 cells ml-1 were present on many

occasions when samples were collected at site a. Low cyanobacterial presence was

recorded here in 2001, but this may result from the site only being sampled in early

summer (June and July) and not throughout the entire bloom season. Cyanobacterial

presence was also low on the five occasions sampled during 2007 (mid monthly from

June to October inclusive). Cyanobacterial presence was also low on most occasions

sampled in 2008, apart from a brief bloom in late August. In terms of biomass however

cyanobacterial presence was low in 2000, 2001 and 2007, when in never exceeded

1000 mg m-3, and also in 2008 when the maximum biomass on the dates sampled never

exceeded 3500 mg m-3. Species composition varied annually, and although there were

high counts of small non-toxic species such as Aphanocapsa spp., Aphanothece spp.,

Chroococcus spp. and Merismopedia spp. present between 2000 and 2004, these often

contributed little to the total cyanobacterial biomass present. Increased counts of

potentially toxic species of Microcystis occurred between 2002 and 2006, and also in

2008, and these tended to dominate the cyanobacterial biomass present during these

years, with a smaller contribution from potentially toxic species of Anabaena. However

Aphanizomenon flos-aquae had become dominant by late September 2006.

Sites b and d2 were sampled more intensively than site a in 2001, and a major bloom in

excess of 1,000,000 cells ml-1 (Figures 3a, 4a) and 120,000 mg m-3 of cyanobacterial

biomass (Figures 3b, 4b) was recorded at both, especially in September. This bloom

was dominated by Aphanizomenon flos-aquae and Microcystis sp. at both sites, in terms

of both cell counts and biomass. Smaller blooms with lower cell counts and biomass,

but generally in excess of 200,000 cells ml-1 and 20,000 mg m-3 biomass occurred at site

b from 2002 through to 2005, and again in 2008 (Figure 3a, b, Appendix C). These



A) Abundance

B) Biomass

Figure 2 Total cyanobacterial abundance, as cell numbers (A) and as biomass (B) at site a in the Québec portion of Lake Champlain. Note the change in Y axis scale from Figures 3, 4 and 5



A) Abundance

B) Biomass

Figure 3 Total cyanobacterial abundance, as cell numbers (A) and as biomass (B) at site b in the Québec portion of Lake Champlain. Note the change in Y axis scale from Figures 2, 4 and 5



A) Abundance

B) Biomass

Figure 4 Total cyanobacterial abundance, as cell numbers (A) and as biomass (B) at site d2 in the Québec portion of Lake Champlain. Note the change in Y axis scale from Figures 2, 3 and 5



blooms often displayed a considerable mix of cyanobacterial species present, with

Anabaena spiroides being more common in 2002 and several Microcystis species being

dominant in subsequent years. Chroococcus dispersus and Phormidium mucicola were

the more common non-toxic taxa present during these years. Cyanobacterial presence

at site b during 2006 and especially during 2007 was often low.

Apart from 2001, site d2 was much less frequently sampled than the other sites. A small

bloom of mixed cyanobacterial species was recorded there in early August 2004, and a

larger bloom dominated by Anabaena spiroides was measured in early August 2006

(Figure 4a, b, Appendix C).

Blooms sampled at site d8 in the north-west corner of Missisquoi Bay near Venise-en-

Québec (in Baie de Venise) generally had a lower cyanobacterial abundance both as

cell counts and total cyanobacterial biomass than sites b and d2 on the eastern side of

the bay. Blooms rarely exceeded 100,000 cells ml-1 or had a total cyanobacterial

biomass greater than 5000 mg m-3 (Figure 5a, b). Anabaena spiroides was dominant in

2002, with a smaller contribution from non-toxic Chroococcus dispersus. In 2004 the cell

counts at site d8 were dominated by non-toxic Aphanocapsa delicatissima, Aphanothece

clathrata and Chroococcus dispersus, although Microcystis species were the dominant

contributors to the total cyanobacterial biomass. Various Microcystis species were also

major contributors to total cyanobacterial biomass in 2003, 2004, 2005 and 2006. Only

a small cyanobacterial biomass was recorded at site d8 during 2007 and 2008, although

Aphanothece clathrata contributed to very high cell counts of non-toxic cyanobacteria in

early August 2008 (Figs 5a, b, Appendix C).

The contribution of potentially toxic species varied considerably both within and between

sites, and is dependent in part on how abundance is measured, whether as cell counts

or as biomass. For example at site a in 2000, potentially toxic cyanobacteria contributed

less the 10% of the total cyanobacterial cell count, but over 50% of the total biomass

because the cell sizes of many of the toxic species present are considerably larger than

the cell sizes of the non-toxic species present. At other times when there are large

numbers of potentially toxic cells present, they can dominate both cell count and

biomass measures, such as at most sites in 2003, 2004, 2005 and 2006.

3.2 Contribution of Cyanobacteria to the Total Phytoplankton Biomass

The contributions provided by cyanobacteria to total phytoplankton biomass are shown

for site a in Figure 6. Cyanobacteria comprised less than 15% of the total phytoplankton

biomass in all samples collected at site a in 2000, 2001 and in 2007. The phytoplankton

community in these years was usually dominated by diatoms (also called

Bacillariophyceae) (Cyclotella, Stephanodiscus, Fragellaria and Melosira) and

Cryptophyceae (Cryptomonas, Rhodomonas). The Chlorophyceae (green algae) genus

Pandorina was dominant in August 2007 and the dinoflagellate Peridinium was dominant

in September that year.

Cyanobacterial blooms at site a in other years (2002 – 2006, and 2008) were generally

preceded by a mixed community comprised mainly of diatom and cryptophyte species.

During the blooms, cyanobacteria at times contributed more than 80% of the total

phytoplankton biomass, but the cyanobacteria were usually progressively replaced by

diatoms (and some Cryptophyceae) as the bloom declined. Various Chrysophyceae

species were also frequently present, but usually at low biomass.



A) Abundance

B) Biomass

Figure 5 Total cyanobacterial abundance, as cell numbers (A) and as biomass (B) at site d8 in the Québec portion of Lake Champlain. Note the change in Y axis scale from Figures 2, 3 and 4



Figure 6 Contribution provided by various phytoplankton divisions (both cyanobacteria and eukaryotes) to total phytoplankton biomass at site a, from 2000 to 2008 inclusive. Note the difference in Y axis scale from Figures 7, 8 and 9

Phytoplankton presence at site b showed a similar pattern (Figure 7). Data for spring in

most years shows only a low total phytoplankton biomass present, composed mainly of

the same diatom and Cryptophyceae species as at site a. 2001 however differs from

site a as site b was sampled more frequently and later in summer and autumn than site

a, and the biomass was up to an order of magnitude higher in many cases (note the

change in Y axis scale from Figure 6 to Figure 9). The greater sampling frequency at site

b was possibly due to the cyanobacterial bloom at this site being of greater management

concern than any bloom at site a. Exponential growth of cyanobacteria usually took

place in late spring or summer, to comprise more than 80% of the phytoplankton

biomass. They were then gradually replaced by diatoms or Cryptophytceae algae as

total biomass decreased in late summer and early autumn. As at site a, there was little

development of a cyanobacterial bloom at site b in 2007, but a bloom of the

dinoflagellate Peridinium occurred instead.

There are generally insufficient data to examine patterns of succession at site d2, except

for 2001 (Figure 8). Cyanobacterial blooms persisted here all summer, gradually

increasing to a maximum on the 26th of September, when they comprised more than

70% of the total phytoplanktonic biomass. The bloom had disappeared by mid October,

when diatoms once more dominated. However by this time the total phytoplanktonic

biomass was small. Chlorophyceae algae (Cosmarium, Planktosphaeria) dominated in

July 2008, and the dinoflagellate Peridinium was also dominant at sampling occasions in

2007.



Figure 7 Contribution provided by various phytoplankton divisions (both cyanobacteria and eukaryotes) to total phytoplankton biomass at site b, from 2000 to 2008 inclusive. Note the difference in Y axis scale from Figures 6, 8 and 9

Figure 8 Contribution provided by various phytoplankton divisions (both cyanobacteria and eukaryotes) to total phytoplankton biomass at site d2, from 2000 to 2008 inclusive. Note the difference in Y axis scale from Figures 6, 7 and 9



The phytoplankton community at site d8 (Figure 9) also followed a similar pattern to that

at sites a and b. However, cyanobacterial blooms at this location were often brief and of

low biomass. In 2002 cyanobacteria dominated throughout the summer, usually

contributing greater that 70% of the total phytoplankton biomass. Cyanobacteria

contributed more than 50% of the total phytoplankton biomass on only one or two

sampling occasions in most other years, and more commonly contributed less than 30%.

There were few cyanobacteria present in 2006, 2007 and 2008. A bloom of the green

algae Closterium and Mougeotia, and of the Cryptophyceae genus Cryptomonas were

present in June 2006. The dinoflagellate Peridinium was also dominant at this location

in September 2007, although total phytoplanktonic biomass was low on this occasion.

Overall however, cyanobacteria generally contributed more than 80% of the total

phytoplanktonic biomass once this exceeded 25,000 mg m-3 (Figure 10).

3.3 Interactions between phytoplankton and cyanobacterial taxa

Log transformed (log(x)) biomass data for significant taxa or groupings were subjected to

Spearman Rank Order Correlation (Rho) analysis. The results where significance was

indicated (at 0.05) are tabulated in Appendix D; however many, especially with a

correlation coefficient (Rho) of <0.50, are very weak and unlikely to indicate much of an

interaction between the taxa concerned. Some examples are also shown as scatter

plots in Figure 11. Total eukaryotic phytoplankton biomass was usually not correlated

with any cyanobacterial taxa or groupings. Likewise, most commonly occurring

eukaryotic genera were indicated (Rho <0.50) as being only weakly positively correlated

Figure 9 Contribution provided by various phytoplankton divisions (both cyanobacteria and eukaryotes) to total phytoplankton biomass at site d8, from 2000 to 2008 inclusive. Note the difference in Y axis scale from Figures 6, 7 and 8.



Figure 10 The percentage of the total phytoplanktonic biomass contributed by cyanobacteria (sites a, b, d2 and d8, combined)

with total cyanobacterial biomass or with individual species of cyanobacteria. This

probably results from most samples being collected during summer when cyanobacteria

greatly dominate the total phytoplankton biomass, and often a smaller biomass of these

eukaryotic taxa is present (Figure 11a). In addition, possible negative relationships

resulting from seasonal succession within the phytoplankton community (e.g. spring and

autumn dominance by eukaryotic algae when cyanobacteria are uncommon) have not

been shown due to the exclusion of samples with missing data (when a taxon was not

recorded in a sample) from the correlation analysis.

Most cyanobacterial taxa contributed significantly (Rho between 0.64 and 0.90,

Appendix D) to the total cyanobacterial biomass present, but less strongly to the total

phytoplankton biomass. Anabaena flos-aquae was frequently the major contributor to

total Anabaena biomass (Rho = 0.85), as was Microcystis aeruginosa to total Microcystis

biomass (Rho = 0.99). Although many cyanobacterial taxa occurred simultaneously

together in Missisquoi Bay, the strength of the correlation coefficients between them

varied considerably (Appendix D). Only Anabaena spiroides was not significantly

correlated to most other taxa. Likewise, while the examples provided in Figure 11

indicate possible positive trends between several of the cyanobacterial taxa occurring in

Missisquoi Bay, they mostly show a high degree of scatter further revealing that any

relationships between the taxa are generally not strong. While these analyses provide

an indication of which taxa co-occur more frequently together in Missisquoi Bay and that

their contributions to total cyanobacterial and total phytoplankton biomass in summer

can be considerable, overall there is much variation in the strengths of these

interactions.



3.4 Cyanobacteria and toxin presence

Total microcystin-LR concentrations (intracellular plus extracellular) at site a were

generally low, and only exceeded 1.0 µg l-1 on two occasions, in July 2005 and August

2008 (Figure 12). Similarly low concentrations were also recorded at site d8, with

concentrations there only exceeding 1.0 µg l-1 in late July 2005 (Figure 15). Higher

concentrations, exceeding 5 µg l-1 occurred at site b in August 2002 and in July 2008

(Figure 13) and exceeding 12 µg l-1 in July 2005. Likewise a total microcystin-LR

Figure 11 Examples of some of the interactions between various species or groupings of phytoplankton within Missisquoi Bay. Spearman Rank Order Correlation coefficients (Rho) (with missing data excluded), number of observations (n) and probabilities (p) for each of the five panels are as follows: Panel a - total eukaryotic phytoplankton biomass and total cyanobacterial biomass (Rho = 0.01, n = 148, p = 0.86); Panel b – total Anabaena biomass and total Microcystis biomass (Rho = 0.49, n = 87, p < 0.0001); Panel c – Anabaena flos-aquae biomass and Aphanizomenon flos-aquae biomass (Rho = 0.40, n = 44, p = 0.007); Panel d – Anabaena flos-aquae biomass and Microcystis sp. biomass (Rho = 0.33, n = 42, p = 0.03); and Panel e – Aphanizomenon flos-aquae biomass and Microcystis sp. biomass (Rho = 0.75, n = 44, p < 0.0001). Note that for a significant number of samples there were missing data for one of the two groupings or species in most panels. Using log (x + 1) transformation allows these to be shown as 0 on either axis.



concentration greater than 10 µg l-1 occurred at site d2 in early October 2001 (Figure

14). The higher concentrations (i.e > 0.6 µg l-1) of microcystin-LR were found when total

Microcystis biomass exceeded 300 mg m-3 (data not shown). On almost all sampling

occasions, the majority (>80% and often >90%) of the total microcystin-LR was

contained within the cyanobacterial cells, rather than as free microcystin-LR within the

water column (data not shown).

Concentrations of total microcystin-RR and total microcystin-YR (intracellular plus

extracellular) also occurred together with microcystin-LR on some occasions, and at

times concentrations of these microcystins exceeded those of microcystin-LR. In

particular, total microcystin-RR was present at its highest concentrations (1.95 µg l-1 at

site a, 2.94 µg l-1 at site b and 0.624 µg l-1 at site d8) in July 2005 (Figure 12, 13 and 15,

respectively). The highest concentration at site d2 was 0.60 µg l-1 in August 2006 (this

site was not sampled in 2005). High total microcystin-YR concentrations of 0.70 to 1.11

µg l-1 occurred at sites a and d2 in 2004 (Figures 12 and 14), while a concentration of

2.083 µg l-1 was measured for site b in mid July 2005 (Figure 13). On most occasions

sampled intracellular concentrations of both microcystin-RR and microcystin-YR

predominated.

Figure 12 Presence of microcystin-LR, microcystin-RR and microcystin-YR at site a. Note the difference in Y axis scale from Figures 13, 14 and 15



Figure 13 Presence of microcystin-LR, microcystin-RR and microcystin-YR at site b. Note the difference in Y axis scale from Figures 12, 14 and 15

Figure 14 Presence of microcystin-LR, microcystin-RR and microcystin-YR at site d2. Note the difference in Y axis scale from Figures 12, 13 and 15



Figure 15 Presence of microcystin-LR, microcystin-RR and microcystin-YR at site d8. Note the difference in Y axis scale from Figures 12, 13 and 14

As can be seen in Figures 12 to 15, microcystin-LR is the variant most often detected,

nevertheless the relationships between concentrations of total microcystin, microcystin-

LR, microcystin-RR and microcystin-YR were investigated by grouping all the data from

sites a, b, d2 and d8 together, removing any values that were at or below the level of

detection and converting the data to a logarithmic scale (to allow for better presentation

due to the skewed distribution of the data). Scatter plots (Figure 16) and correlation

analyses (Table 1) of each microcystin variant against the others indicate that the

concentrations of microcystin-LR and microcystin-RR, and to a lesser extent of

microcystin-LR and microcystin-YR, tend to increase approximately in unison. However,

apart from the correlation between microcystin-LR and microcystin-RR (Rho = 0.70), the

other relationships were weak and that between microcystin-RR and microcystin-YR did

not even reach significance.

Total cyanobacterial biomass was significantly positively correlated (at the 5% level of

significance or lower) with total microcystin concentration and also with microcystin-LR,

microcystin-RR and microcystin-YR concentrations (Table 1), although Rho was

generally below 0.50. Total Microcystis biomass and Microcystis aeruginosa biomass

however were frequently more strongly correlated with the microcystin variants (Rho

usually > 0.50). Total microcystin concentration and the concentrations of microcystin-

LR, microcystin-RR and microcystin-YR all tended to increase with increasing total

Microcystis biomass (Figure 17), but similar patterns were present for Microcystis

aeruginosa and Microcystis sp. In fact Microcystis aeruginosa biomass has the highest

correlations with microcystin concentrations, either total or by variant (Table 1). M.

aeruginosa was the dominant cyanobacterial species present in terms of biomass in

37% of samples where total microcystin concentrations exceeded 1 μg l-1, including



Figure 16 Concentrations of microcystin-LR, RR and YR compared against each other, for sites a, b, d2 and d8 on Missisquoi Bay combined, presented on a logarithmic scale. Data below the level of detection have been removed. Spearman Rank Order Correlation coefficients (Rho), number of observations (n) and and probabilities (p) for each of the three panels are as follows: MC-LR and MC-RR, Rho = 0.70, n = 47, p <0.0001; MC-RR and MC-YR, Rho = 0.29, n = 44, p = 0.06; MC-LR and MC-TR, Rho = 0.43, n = 47, p = 0.003.

Table 1 Spearman Rank Order Correlation coefficients (Rho) for variants of microcystins and for the biomass of Microcystis spp, Anabaena spp. and Aphanizomenon flos-aquae for the combined data collected from sites a, b, d2 and d8 in the Québec portion of Missisquoi Bay. The data were log transformed with values below the level of detection excluded prior to the analysis. Note: No attempt was done to adjust for multiple statistical significance testing, therefore some correlations presented below might be declared significant by chance alone.

Attributes n Rho p

MC-LR and MC-RR 47 .70 <.001

MC-RR and MC-YR 44 .29

MC-LR and MC-YR 47 .43 .003

Total Cyanobacterial Biomass and Total MC 84 .44 <.001

Total Cyanobacterial Biomass and MC-LR 84 .49 <.001

Total Cyanobacterial Biomass and MC-RR 47 .55 <.001

Total Cyanobacterial Biomass and MC-YR 47 .33 .02

Total Microcystis Biomass and Total MC 70 .64 <.001

Total Microcystis Biomass and MC-LR 70 .62 <.001

Total Microcystis Biomass and MC-RR 43 .48 .001

Total Microcystis Biomass and MC-YR 43 .48 .001



Microcystis aeruginosa Biomass and Total MC 20 .86 <.001

Microcystis aeruginosa Biomass and MC-LR 20 . 83 <.001

Microcystis aeruginosa Biomass and MC-RR 19 .53 .02

Microcystis aeruginosa Biomass and MC-YR 16 .73 .001

Microcystis flos-aquae Biomass and Total MC 7 .61

Microcystis flos-aquae Biomass and MC-LR 7 .50

Microcystis flos-aquae Biomass and MC-RR 7 .43

Microcystis flos-aquae Biomass and MC-YR 7 .61

Microcystis sp. Biomass and Total MC 48 .48 <.001

Microcystis sp. Biomass and MC-LR 48 .56 <.001

Microcystis sp. Biomass and MC-RR 22 .47 .03

Microcystis sp. Biomass and MC-YR 23 .11

Total Anabaena Biomass and Total MC 69 .21

Total Anabaena Biomass and MC-LR 69 .26 .03

Total Anabaena Biomass and MC-RR 41 .23

Total Anabaena Biomass and MC-YR 40 .21

Anabaena flos-aquae Biomass and Total MC 47 .15

Anabaena flos-aquae Biomass and MC-LR 47 .11

Anabaena flos-aquae Biomass and MC-RR 27 .02

Anabaena flos-aquae Biomass and MC-YR 28 .24

Anabaena spiroides Biomass and Total MC 34 .22

Anabaena spiroides Biomass and MC-LR 34 .28

Anabaena spiroides Biomass and MC-RR 23 .14

Anabaena spiroides Biomass and MC-YR 22 .37

Aphanizomenon flos-aquae Biomass and Total MC 64 .14

Aphanizomenon flos-aquae Biomass and MC-LR 64 .24

Aphanizomenon flos-aquae Biomass and MC-RR 35 .10

Aphanizomenon flos-aquae Biomass and MC-YR 35 -.12



Figure 17 Concentrations of microcystin-LR, RR and YR and the total of all three variants compared against total Microcystis biomass for sites a, b, d2 and d8 in Missisquoi Bay combined, presented on a log scale. Data below the level of detection have been removed. Spearman Rank Order Correlation coefficients (Rho), number of observations (n) and and probabilities (p) for each of the four panels are as follows: Total Microcystis biomass and total MC, Rho = 0.64, n = 70, p <0.0001; Total Microcystis biomass and MC-LR, Rho = 0.62, n = 70, p <0.0001; Total Microcystis biomass and MC-RR, Rho = 0.48, n = 43, p = 0.001; Total Microcystis biomass and MC-YR, Rho = 0.48, n = 43, p = 0.001.

when the highest concentration of 17.3 μg l-1 was measured at site b in July 2005.

Likewise Microcystis sp. was the dominant Microcystis species present in 40% of the

samples where total microcystin concentrations exceeded 1 μg l-1, but it was less

strongly correlated with total microcystin concentration and with concentrations of

microcystin-LR and microcystin-RR (Table1). Even if the strength of the relationships

appear relatively similar, the positive correlations were however not significant between

M. flos-aquae and the microcystin concentrations because the sample size was small.

In 2004 Microcystis viridus was the main species present when total microcystin

concentrations exceeded 1 μg l-1 but there are insufficient data to test correlations

between this species and microcystin concentrations.

Generally microcystin concentrations were low when other taxa dominated the

cyanobacterial community. One exception occurred at site d2 on the 2nd of October

2001, when 10.809 μg l-1 of total microcystin were measured and Aphanizomenon flos-

aquae comprised 71% of the cyanobacterial biomass. Intracellular microcystin was

greater than 90% at the time, indicating that it was not residual microcystin dissolved in

the water column from a former bloom. Aphanizomenon flos-aquae is generally not

recognised as a microcystin producer in the literature (Sivonen and Jones, 1999;

Heussner et al., 2012), and no study has been undertaken in Québec to determine if it is

associated with microcystin production there. However because of its potential to

produce neurotoxins elsewhere (Sivonen and Jones, 1999), it is regarded as a



potentially toxic species in Québec (Blais and Sinotte, 2008). In early October 2001

most of the remaining 29% of potentially toxic cyanobacteria at site d2 was Microcystis

sp., which was nevertheless present at high biomass (25,282 mg m-3) and most likely

produced the microcystin on this occasion. Higher microcystin concentrations also

occurred at site b in August 2002 and at site d2 in August 2006 when Anabaena

spiroides was dominant in terms of biomass. Despite this, correlation analysis found no

significant correlations between Anabaena spirodes and any microcystin concentration,

or between Aphanizomenon flos-aquae and any microcystin concentration (Table 1).

Relationships between microcystin presence and a range of physico-chemical attributes

of the lake water were also examined. Microcystin concentrations may possibly co-vary

with environmental factors in the same manner as cyanobacterial biomass, especially

total Microcystis biomass. For example, microcystin concentrations (microcystin-LR,

microcystin-RR, microcystin-YR, and total microcystin) were significantly positively

correlated with increasing chlorophyll-a concentration and also with pH and turbidity

(Table E1 of Appendix E). The majority of the correlation coefficients (Rho) were

however weak, with a value of under 0.50. The correlations with chlorophyll-a and

turbidity may simply be due to increasing concentrations of toxin-producing

cyanobacteria within the water column, which would cause increases in both chlorophyll-

a concentrations and turbidity. Increased photosynthesis by increased cyanobacterial

biomass also causes pH to increase. Total microcystin, microcystin-LR, microcystin-RR

and microcystin-YR were also weakly (Rho < 0.50) negatively correlated with euphotic

depth, possibly resulting from increasing cyanobacterial biomass (with associated

increased microcystin production) causing a reduction in light penetration through the

water column. Scatter diagrams of total microcystin concentrations against various other

water quality attributes can be also found in Appendix E.

Microcystin-LR, microcystin-YR and total microcystin concentrations were also positively

correlated with total phosphorus concentrations, while total microcystin (seemingly

driven by microcystin-YR) was weakly correlated with the TN:TP ratio, and microcystin-

LR appeared negatively correlated (n = 18) with oxidised nitrogen concentrations. Most

of these correlations were however of borderline significance (Table E1 of Appendix E),

as Rho was frequently less than 0.50. Although microcystin-RR and microcystin-YR

concentrations both showed a significant positive correlation with water temperature,

being present mainly when the temperature was above 20°C (Figure 18), this was weak

(Rho again < 0.50), and water temperature appears to have little influence on

microcystin-LR concentrations.

Concentrations of saxitoxin, neosaxitoxin and cylindrospermopsin were always below the

detection limit of the analytical method. Anatoxin-a was detected only once in 180

samples that were analysed for this toxin.

3.5 Eukaryotic and cyanobacterial presence and water quality indicators

3.5.1 Analysis of the combined data for sites a, b, d2 and d8

Cyanobacterial and other commonly occurring eukaryotic phytoplankton (Chlorella,

Closterium, Cosmarium, Cryptomonas, Cyclotella, Fragilaria, Melosira, Oocystis,

Peridinium, Planktosphaeria, Rhodomonas, Scenedesmus and Stephanodiscus) have

been examined against a range of water quality attributes. Chlorella, Oocystis and

Scenedesmus were chosen as they occurred in >40% of samples, whereas the other



Figure 18 Variations of concentrations of total microcystin, MC-LR, MC-RR and MC-YR at sites a, b, d2 and d8 in Missisquoi Bay combined in relation to water temperature. Microcystin data are presented on a log scale, and data below the level of detection have been removed. Spearman Rank Order Correlation coefficients (Rho), number of observations (n) and and probabilities (p) for each of the four panels are as follows: Total microcystin and surface water temperature, Rho = 0.20, n = 82, p = 0.08; MC-LR and surface water temperature, Rho = 0.08, n = 82, p = 0.47; MC-RR and surface water temperature, Rho = 0.43, n = 45, p = 0.003; MC-YR and surface water temperature, Rho = 0.47, n = 45, p = 0.001.

genera were sometimes dominant (see Section 3.2.). The data were log transformed

(log(x)) where necessary because of the skewed distributions of the raw data and

subjected to Spearman Rank Order Correlation analysis. The following is a summary of

the results which were significant at the 5% level of significance or less, as shown in

Appendix F (Table F1). Scatter plots for some relationships that the correlation analysis

indicated to be significant are also shown in Appendix F.

Some common patterns emerged from the correlation analysis. Total phytoplankton,

and total cyanobacterial biomass were positively correlated with chlorophyll-a (Rho =

0.78 and 0.87, respectively), as expected as both biomass and chlorophyll-a are

indicators of phytoplankton presence, but chlorophyll-a was much less strongly

correlated with total eukaryotic algal biomass (Rho = 0.28), probably due to the

frequently low biomass of the eukaryotic taxa. All the cyanobacterial taxa were also

significantly positively correlated with chlorophyll-a wih a correlation coefficient (Rho) of

greater than 0.50, (Table F1, Appendix F) and many of the individual eukaryotic algal

taxa were also correlated, but less strongly.

Total cyanobacterial biomass and the biomass of many of the individual cyanobacterial

taxa (but not all) were significantly positively correlated with phycocyanin concentrations.

In particular, the strongest correlations (Rho > 0.50) were for Aphanizomenon flos-

aquae, Microcystis sp., and Microcystis aeruginosa. The positive correlations with the



two eukaryotic algal taxa are probably due to chance or their occurrence at the same

time as cyanobacterial, as these taxa should not contain phycocyanin.

Most cyanobacterial biomass indicators were significantly positively correlated (Rho

often > 0.5) with total phosphorus concentrations, with higher biomass associated with

higher concentrations of this major nutrient. Although total eukaryotic algal biomass was

also positively (but weakly) correlated with increasing total phosphorus, there were few

significant correlations between total phosphorus and the eukaryotic taxa tested (Table

F1, Appendix F), and those that were present are weak. There were few significant

correlations between either cyanobacteria or eukaryotic algal taxa and total nitrogen.

Many of the cyanobacterial biomass indicators were however significantly negatively

correlated with oxidised nitrogen (NOx) concentrations with Rho values between -0.63

and -0.85, suggesting that oxidised nitrogen is not a main source of nitrogenous nutrient

for the cyanobacteria in Missisquoi Bay, or that NOx is rapidly utilised. The biomass of

many cyanobacterial taxa was also significantly negatively correlated with the TN:TP

ratio (with Rho values between -0.32 and -0.56), apparently reflecting the positive

correlations with TP in the univariate analysis described above (Table F1, Appendix F).

Few eukaryotic algal indicators were significantly correlated with total nitrogen, NOx and

with the TN:TP ratio.

Increasing cyanobacterial biomass and eukaryotic algal biomass were frequently

significantly and positively correlated with pH, often with correlation coefficients (Rho)

above 0.50, and likewise cyanobacterial biomass with turbidity. Higher pH can be

attributed to the effects of photosynthesis, although cyanobacteria are also advantaged

by a pH of 8.0 or more (Shapiro, 1990). Increased turbidity could be due in part to the

increased number of cyanobacterial cells and colonies entrained within the water

column. Increasing water temperature may also favour increased biomass of the more

common cyanobacterial and eukaryotic algal taxa present in Missisquoi Bay, although

correlations were weaker (Rho often <0.50). Several groupings (Anabaena taxa in

particular) were positively correlated with dissolved organic carbon. Euphotic depth was

also negatively correlated with almost all taxa and groupings of cyanobacteria and with

three eukaryotic algal taxa, although the strength of these correlations varied

considerably (Table F1, Appendix F). This may be due to increased phytoplankton

presence within the water column reducing water column transparency and light

penetration (turbidity and euphotic depth were also significantly negatively correlated).

Electrical conductivity and dissolved oxygen had little effect on either cyanobacterial or

eukaryotic algal biomass.

3.5.2 Site by site analysis

Although there were often few oxidised nitrogen and ammonia data available for some

sites (Table 2), Spearman Rank Order Correlation analysis were also undertaken on a

site by site basis to determine if there were different responses between various

cyanobacterial and eukaryotic algal taxa and the water quality variables at different sites.

This was because some water quality variables vary spatially across the four sites on

Missisquoi Bay (Table 2). In particular, sites b and d2 tended to have higher TP

concentrations than sites a and d8, as well as slightly higher pH values, higher turbidity,

lower TN:TP ratios and shallower Secchi disc depths.

The results of the site by site correlation analysis are provided in Table F2, Appendix F.

Note that Table F2 provides only a summary of those correlations found to be significant



Table 2 90th

percentile, median (in bold) and 10th

percentile values for various water quality variables at different sites on Missisquoi Bay. Number of observations (n) in brackets

Water quality variable

Site a Site b Site d2 Site d8

TP (µg l-1

) 61.3-43.5-21.2

(44)

100.5-60.3-39.0

(46)

126.4-81.6-42.8

(13)

53.5-35.5-22.0 (36)

TN (mg l-1

) 0.77-0.35-0.23

(41)

1.01-0.35-0.23 (45) 0.69-0.37-0.27 (14) 0.76-0.32-0.21 (37)

TN:TP (by concentration)

30.8-6.8-5.00

(39)

20.7-6.6-3.0 (43) 10.6-5.7-2.4 (13) 28.3-7.5-4.3 (34)

DOC (mg l-1

) 6.1-5.1-4.2 (21) 6.7-5.9-5.0 (25) 6.4-5.3-4.8 (12) 6.0-5.5-4.9 (13)

Cond (µS cm-1

) 140-131-119 (7) 155-140-137 (10) 139-136-136 (9) (0)

pH 8.88-7.90-7.50

(43)

9.40-8.60-7.60 (49) 9.50-8.75-7.77 (18) 9.10-8.30-7.60 (37)

Turbidity (NTU) 9.9-4.3-2.0 (44) 19.2-8.0-2.4 (29) 34.2-11.0-3.8 (14) 9.7-4.3-2.7 (34)

NH3 (mg l-1

) 0.11-0.05-0.01

(9)

0.07-0.02-0.01 (14) 0.22-0.03-0.01 (9) 0.07-0.05-0.02 (3)

NOx (mg l-1

) 0.43-0.16-0.04

(8)

0.11-0.03-0.01 (11) 0.10-0.02-0.01 (9) 0.10 (1)

Temperature (ºC) 25.0-22.2-16.4

(42)

26.2-22.3-14.6 (47) 26.3-22.7-16.8 (15) 26.0-22.9-16.0 (40)

Secchi (m) 2.5-1.55-1.1 (42) 1.5-1.0-0.6 (49) 1.7-0.9-0.5 (13) 2.5-1.6-0.9 (40)

DO (mg l-1

) 10.7-9.0-6.9 (25) 11.7-9.6-7.8 (20) 9.3-8.0-6.4 (4) 10.3-8.4-6.3 (20)

at a 0.05 level of significance or less, and gives no indication of the strength of these

correlations or the probabilities. Some may be weak correlations or occur by chance

alone. Generally the trends were the same for each taxa or grouping across the four

sites, and for when all sites were combined (Table F1), especially with the

cyanobacterial taxa, although some site to site variation did occur (significant

correlations at some sites but not at others). Often each taxa was significantly correlated

with a particular environmental variable at more than one site, and sometimes at all four

sites. A number of the cyanobacterial taxa or groupings were significantly positively

correlated with TP, especially at sites b and d2. Only a few taxa were negatively

correlated with TN and many cyanobacteria were negatively correlated with the TN:TP

ratio, these correlations occurring especially at sites a and b. There were a number of

significant positive correlations between some cyanobacterial taxa and DOC, particularly

at sites b and d2, and also with pH at all sites. Many of the cyanobacterial taxa were

positively correlated with turbidity and at the same time negatively correlated with Secchi

disc depth, suggesting that a higher abundance of cyanobacteria in the water increases

turbidity and reduces light penetration.

3.6 Meteorological factors and cyanobacterial and eukaryotic algal presence

Six meteorological factors that may influence phytoplankton distribution and abundance

in Missisquoi Bay were used in Spearman Rank Order Correlation analysis against the

biomass data for 9 cyanobacterial taxa or groupings and 14 eukaryotic algal genera.

The phytoplankton data displayed a highly skewed distribution and were log transformed



(log(x)) prior to use. Four timeframes (day of sampling, 4 day antecedent, 7 day

antecedent, 10 day antecedent) were available for the meteorological data. Analyses

were performed for each site (a, b, d2 and d8) and for the 4 sites combined

Only the correlations found to be significant at a probability of ≤0.05 are shown in the

tables in Appendix G. Most correlations between the meteorological variables and the

phytoplankton taxonomic groupings were not significant. Of the cyanobacteria that were

significant, most were weakly correlated (Rho < 0.50), and sometimes the significance

possibly results from the high number of observations involved (the higher the n, the

lower the coefficient value that becomes significant at the 5% level of significance),

especially when all sites were combined. Moreover, due to the multiple correlations

tested, some significance by chance alone cannot be excluded. Examples of

scatterplots where the strongest correlations were obtained between cyanobacteria

(from all four sites combined) and meteorological conditions are shown in Figure 19.

With the exception of Aphanizomenon flos-aquae, maximum and minimum air

temperatures when significantly correlated to cyanobacterial presence were positively

correlated. Significant correlations for minimum air temperature and for maximum air

temperature frequently coincided. The greater number of significant correlations

between the 7 day mean antecedent maximum temperature and cyanobacterial taxa

suggest that a week or more of hotter weather prior to sampling may lead to increased

cyanobacterial presence. Maximum air temperature on the same day of sampling was

only occasionally correlated with most cyanobacterial presence. Significant correlations

were found on occasions for all sites seperately, although the data are generally

insufficient to make a confident assessment of the effect of air temperature on

cyanobacterial distributions across the bay.

There were few significant correlations between cyanobacteria and wind direction.

Cyanobacterial presence tended to be negatively correlated with wind speed. Longer

antecedent conditions (10 days) of stronger winds are more likely to reduce

cyanobacterial presence than shorter periods, especially with the Anabaena species.

The results of the correlation analysis are insufficient to be conclusive about spatial

effects of either wind direction or wind velocity on the distribution of cyanobacteria in

Missisquoi Bay.

Both rainfall and cloud cover may provide measures of the amount of sunshine received

by phytoplankton within Missisquoi Bay, as possibly fewer cyanobacteria may be present

on cloudy and rainy days when solar radiation is reduced. Rainfall showed few

correlations with cyanobacterial presence, although total cyanobacterial biomass may

have been reduced by rainfall on the same day of sampling, and if there was higher

rainfall across the preceeding 10 days. However the correlation coefficients (Rho) are

weak, all being below -0.39. In comparison, Anabaena spiroides and Microcystis

aeruginosa showed a slight increase in response to rain, especially on the day of

sampling. Increased cloud cover may reduce both total cyanobacterial biomass plus that

of a number of common species, especially if these conditions persist for a week or

more. However again the significant correlations are few and mostly represent weak

relationships between the attributes.

Several genera of eukaryotic algae also showed responses to meteorological conditions,

in particular to the maximum temperature. Several taxa appear to be favoured by hotter

air temperatures whereas Rhodomonas and to a lesser extent Cryptomonas were



Figure 19. Examples of scatterplots where the strongest correlations between cyanobacterial biomass at sites a, b, d2 and d8 combined and meteorological conditions were usually obtained, except wind speed (panel f). Note that in most panels there are a number of samples where there is missing cyanobacterial biomass data. Using log (x + 1) transformation allows these to be shown as 0 on the Y axis. Spearman Rank Order Correlation coefficients (Rho), number of observations (n) and probabilities (p) for each of the six panels are as follows: Total Anabaena biomass and antecedent 10 day maximum temperature, Rho = 0.22, n = 94, p = 0.03; A spiroides biomass and antecedent 10 day minimum temperature, Rho = 0.36, n = 38, p = 0.02; Total cyanobacterial biomass and antecedent 10 day rainfall, Rho = -0.26, n = 132, p = 0.003; Total cyanobacterial biomass and antecedent 10 day cloud cover, Rho = -0.25, n = 132, p = 0.004; M. aeruginosa biomass and antecedent 10 day wind direction, Rho = -0.62, n = 22, p = 0.002; M. aeruginosa biomass and antecedent 10 day wind velocity, Rho = -0.14, n = 22, p = 0.52.

reduced by them, especially over 7 day and 10 day antecedent periods. Total eukaryotic

algal presence tended to increase with prolonged periods of stronger wind, again more

frequently during the 7 or 10 days antecedent periods. Rhodomonas was one genus

that showed significant positive correlations to extended periods of higher wind velocity.

The effect of wind direction on eukaryotic algal taxa was generally not significant or

consistant over time, sites or taxa, suggesting many of the relationships were by chance.

Likewise there were no discernable effects of either rainfall or cloud cover.

Total cyanobacterial biomass, total Anabaena biomass, Aphanizomenon flos-aquae

biomass and total Microcystis biomass were plotted against mean monthly maximum

temperature, mean monthly minimum temperature, mean monthly rainfall, mean monthly



cloud cover, mean monthly wind direction and mean monthly wind velocity for June to

October each year (2001 to 2008). This was to see if annual variations in meteorological

conditions may explain the annual variation shown by the cyanobacterial community in

Missisquoi Bay. Plots of total cyanobacterial biomass against rainfall, cloud cover, wind

direction and wind velocity are given in Figure 20 as examples. The results were

generally inconclusive, although possibly higher rainfall and greater cloud cover in 2007

and 2008 suggest a lower cyanobacterial presence in those years. In addition average

wind direction in both these years was more southerly (150 to 200 degrees) compared to

that of the previous six years (100 to 150 degrees) and velocity slightly lower (10 to 12

km hr-1 compared to 15 km hr-1 and sometimes higher in earlier years), which may also

have had a slight effect on cyanobacterial presence. Similar results were obtained when

cyanobacterial presence was examined against mean monthly meteorological conditions

on a site by site basis (site d2 was not examined as there were too few cyanobacteria

data available).

In reality, it is most likely that a number of these meteorological attributes acting together

would have the greatest impact on cyanobacterial and eukaryotic algal presence in

Missisquoi Bay. For example, cyanobacterial presence would likely be enhanced by

warm sunny calm conditions, and reduced by prolonged cloudy, wet, cold and windy

conditions.

Figure 20 Total cyanobacterial presence for all four Missiquoi Bay sites combined (blue diamonds) compared against various meteorological variables from Philipsburg manual weather station (histogram bars)



3.7 Multivariate analysis of the cyanobacterial and environmental data

3.7.1 Preliminary analysis of the complete cyanobacterial data set

Preliminary analysis was undertaken on the complete set of cyanobacterial data from

sites a, b, d2 and d8 combined, for the years 2000 to 2009 inclusive. Unfortunately the

nMDS ordinations had a high stress value (0.23), meaning that the ordination provides

only a limited representation of the multidimensional data distribution when this is

reduced to 2 dimensional space. There was a large amount of overlap in the ordinations

for sites, months and location (east/west), and any differences were not well shown.

However some differences are evident in the ordination for years, with the years 2001 to

2004 appearing more towards the top left of the ordination, and the years 2005, 2007,

2008 and 2009 appearing more towards the bottom and right side of the ordination. The

outputs of the nMDS are provided in Appendix H (Figure H1).

PERMANOVA analysis revealed significant differences in community composition

between sites, years and months. The overall PERMANOVA results were as follows:-

For sites, Pseudo-F value = 1.95, permutation P (P(perm)) = 0.005, unique

permutations = 9905 out of a total of 9999. Pairwise analysis indicated that

differences in community composition between sites were not always significant.

Sites a and d8 were not significantly different from each, as were sites b and d2 and

sites b and d8. However significant differences were found between sites a and b (t

= 1.43, P(perm) = 0.046, unique permutations = 9934), sites a and d2 (t = 1.77,

P(perm) = 0.005, unique permutations = 9936), and sites d2 and d8 (t = 1.70,

P(perm) = 0.005, unique permutations = 9929).

Location on the eastern or western side of the Bay (sites a and d8 compared to sites

b and d2) was significantly different (Pseudo-F value = 3.24, permutation P (P(perm))

= 0.002, unique permutations = 9943). Globally, the sites from the western and

centre of the bay (sites a and d8) and those from the eastern side (sites b and d2)

were internally more similar while both sides of the bay were different. This may

reflect the effect of winds and other meteorological conditions that may push

cyanobacteria towards the eastern shoreline, although this was not indicated by the

analysis of the meteorological data (Section 3.6, above). It may also reflect nutrient

inputs from major tributaries which may affect community composition.

For years, Pseudo-F value = 9.95, permutation P (P(perm)) = 0.0001, unique

permutations = 9859 out of a total of 9999. Pairwise comparisons between years

indicated significant differences between all years except for 2008 and 2009

(pairwaise PERMANOVA results not shown).

For months, Pseudo-F value = 3.84, permutation P (P(perm)) = 0.0001, unique

permutations = 9708 out of a total of 9999. Pairwise comparisons indicated

significant differences between all months sampled except June and July and June

and October (analysis results not shown). This tends to confirm the relative

unpredictability of cyanobacterial proliferation in Missisquoi Bay and suggests the

complexity of underlying causual factors, at least with the sampling protocols used for

this study.

SIMPER analysis indicated only a low within-sites percent similarity in cyanobacterial

communities (Table H1, Appendix H). Anabaena flos-aquae, Aphanizomenon flos-

aquae, Microcystis sp., Phormidium mucicola and Chroococcus dispersus were the most

common species, but their contributions to the cyanobacterial communities varied from



site to site. Between-site similarities were therefore also generally low, ranging from

23.9% for sites a and d8 to 31.0% for sites b and d2 (data not otherwise shown in

Appendix H).

Part of the within-site variation in cyanobacterial community composition would be due to

the annual variations occurring at each site, as shown by SIMPER (Table H2, Appendix

H). Cyanobacterial communities were most similar throughout 2000 (68.7%) and in

2006 (61.3%). Communities were least similar during 2007 and 2008 (24.1% and

27.0%, respectively). Some of this variation would be due to year-to-year variations in

sampling effort; for example in 2000 only site a was sampled, whereas in other years all

sites were sampled. The cyanobacterial community recorded for 2000 also displayed

the least similarity to the communities of most other years as 2000 was dominated by

small Chroococcales species, whereas different species occurred in most other years

except 2007 when Aphanothece spp. were also present in sufficient biomass. The years

most similar to each other were 2003-04 (41.7% similar), 2002-03 (38.6% similar) and

2003 and 2007 (37.3% similar).

SIMPER analysis also indicated low within-month similarities in most instances (Table

F3, Appendix F). Within-month similarity was greatest in September, at 37.0%. May

and November have been deleted due to only 1 and 3 samples respectively for these

months. Chroococcales (Microcystis sp., Aphanocapsa spp., Chroococcus spp.) were

the taxa contributing most to similarities in June, July and October, whereas it was

Nostocales (Anabaena spp., Aphanizomenon flos-aquae) in August and September.

However a range of other taxa were commonly present most months. The two months

most similar were for August and September at 33.6% similarity, and those least similar

were August and October, at 15.1% similarity.

3.7.2 Analysis of the reduced cyanobacterial data set

nMDS ordinations undertaken on the reduced cyanobacterial data set yielded similar

results to those done on the complete data set – the stress value was also high (0.22)

and the ordinations did not reveal much separation between the samples, especially

based on site and month. Some groupings were more apparent for the ordination based

on years (2001 to 2004 samples being more to the right of the ordination, 2008 and 2009

more to the left) and on location (eastern or western side of Missisquoi Bay) (Figure H2,

Appendix H). These results were consistent with those obtained with the larger data set.

PERMANOVA analysis revealed a significant difference in community composition

between sites, years and months. The overall PERMANOVA results were as follows:-


permutations = 9906 out of a total of 9999. However pairwise analysis only indicated

significant differences in community composition between sites a and d2 (t value =

1.61, permutation P (P(perm)) = 0.01, unique permutations = 9952) and between

sites d2 and d8 (t value = 1.79, permutation P (P(perm)) = 0.003, unique

permutations = 9940). There was also a significant difference indicated between the

sites on the eastern side of Missisquoi Bay (sites b, d2) compared to those on the

western side (sites a and d8) (Pseudo-F value = 2.86, permutation P (P(perm)) =

0.006, unique permutations = 9946).





indicated significant differences between all years except 2008 and 2009 (t value =

0.98, permutation P (P(perm)) = 0.44, unique permutations = 9660).


permutations = 9880 out of a total of 9999. Pairwise comparisons indicated

significant differences between all months, although the comparison between July

and October (t value = 1.37, permutation P (P(perm)) = 0.0503, unique permutations

= 9927) was on the significance threshold.

Again, the results of the PERMANOVA analysis of the reduced cyanobacterial data set

were very similar to those undertaken on the complete cyanobacterial data set (see

Section 3.7.1).

SIMPER analysis for the reduced cyanobacterial data set was also similar to that of the

complete cyanobacterial data set. Average within-site similarities were below 30%

except for site d2 where there were fewer samples (39.4% similarity), with the same

species being the major contributors at each site as in the complete data set (Table H1,

Appendix H). Between-site similarities were also generally less than 30%, and the

cyanobacterial community composition at the eastern sites (b and d2) was only 26.8%

similar to the community composition at the western sites (a and d8). Within-year

similarities were also mostly the same as in the larger data set, although with fewer

samples within-year similarities in 2004 and 2005 were greater at 70.2% (7 samples)

and 52.7% (6 samples) respectively (compare with Table H2, Appendix H). Between-

year similarities were also mostly low, at less then 30% similarity (range 45.6% to

10.7%). The most similar cyanobacterial communities occurred in 2003 and 2004,

followed by 2002 and 2003, 2002 and 2004, and 2003 and 2006, while the least similar

years were 2005 and 2007. The reduced data set also showed similarities and species

composition little different to the complete data set. Between month similarities were

also low, with the closest months (35.2% similarity) being August and September.

3.7.3 Analysis of the environmental data set

Principal components analysis (PCA) was undertaken on the environmental data to

determine if differences in environmental conditions occurred in Missisquoi Bay between

sites, years and months. Ordinations showing the first and second principal components

of the PCA are shown in Figure H3, Appendix H. These two principal components

represent 68.4% of the total variation within the data set. The main variables

represented by the first principal component (47.1% of the variation) and their

coefficients were total phosphorus (-0.44), TN:TP (0.36), pH (-0.45), turbidity (-0.46) and

Secchi disc depth (0.44). The main variables represented by the second principal

component (21.3% of the variation) were total nitrogen (-0.72), and the TN:TP ratio (-

0.54).

The ordinations show slight separation between the sites, with samples from sites a and

d8 plotting more to the right side of the ordination, and samples from sites b and d2

located more towards the left of the ordination. This is also better illustrated in the

ordination based on location (east or west side of Missisquoi Bay). Basically sites a and

d8 are distinguished as having lower nutrient concentrations, pH and better water clarity

(increasing Secchi disc depth) compared to sites b and d2. The ordinations show little

variation between years, although possibly 2006 and 2009 may have had better water

clarity. There were few differences in environmental variables indicated by month,

although July would have had slightly lower surface water temperature, but surface

water temperature has little effect on the distribution of samples based on these first two



principal components. September may often have had lower TN concentrations and

TN:TP ratios, but so too may have August and October. This observation suggests that

nitrogen is reduced during late summer and early autumn.

PERMANOVA analysis of the environmental data revealed significant differences in

environmental variables between sites, years and months. The overall PERMANOVA

results were as follows:-


permutations = 9935 out of a total of 9999. However pairwise analysis indicated that

there were no significant differences in environmental variables between sites a and

d8 (t value = 0.99, permutation P (P(perm)) = 0.40, unique permutations = 9946) and

between sites b and d2 (t value = 1.19, permutation P (P(perm)) = 0.22, unique

permutations = 9940). There was also a significant difference between the sites on

the eastern side of Missisquoi Bay (sites b, d2) and on the western side (sites a and

d8) (Pseudo-F value = 13.81, permutation P (P(perm)) = 0.0001, unique

permutations = 9952).



indicated significant differences between most years, exceptions being 2001 and

2005, 2001 and 2007, 2002 and 2005, 2002 and 2007, 2003 and 2005, and 2003

and 2007. Low sample numbers in some years (and thus a low number of unique

permutations) may have led to the lack of significance in the latter two comparisons.


permutations = 9911 out of a total of 9999. Pairwise comparisons between some

months are not reliable due to the low number of samples in these months (June and

November in particular), although significant differences were recorded between July

and August, September and October; between August and October; and between

September and October. Environmental variables were not significantly different

between August and September (t value = 1.63, permutation P (P(perm)) = 0.07,

unique permutations = 9949).

SIMPER analysis indicated highly variable within-site and between-site similarities within

the environmental data set. Different water quality factors appeared to have different

contributions to the environmental conditions at different sites. The main contributing

factors to similarity between samples at each site were surface water temperature at site

a (24%), total nitrogen at site b (27%), turbidity at site d2 (32%) and the TN:TP ratio (by

concentration) at site d8 (34%). In summary, surface water temperature was a factor

contributing to sample similarity at sites a, b and d8; Secchi disc depth and the TN:TP

ratio at sites a and d8; pH at sites a and b; turbidity at sites a, b and d2; TN at sites b, d2

and d8, and TP at sites b and d2. Likewise there was considerable variability in the

contributions of the various environmental factors to within and between year and month

similarity – each year and month varied considerably from the others.

3.7.4 Comparison of the reduced cyanobacterial data set and the environmental data set

The resemblance matrices generated for the cyanobacterial data and the corresponding

environmental data in PRIMER v6 were compared to each other using RELATE. A small

but statistically significant correlation was found between the multivariate data sets

(global statistic Rho = 0.28, P(perm) = 0.001, total permutations = 999). This correlation



is not strong enough to identify water quality as a major determinant of cyanobacterial

community composition. Further analysis using BEST indicated that TN, surface water

temperature and pH were the environmental factors most correlated with the changes in

cyanobacterial community composition. However the data also indicated that there is

considerable other unexplained variance that may be driving cyanobacterial community

composition in Missisquoi Bay.

3.7.5 Comparisons on a site by site basis

A site by site analysis was also undertaken for sites a, b and d8 (there were insufficient

data for site d2), but the results did not differ greatly from those obtained for the

combined sites (a, b, d2, d8) described above, so the analyses are not reported in any

detail here. The environmental data for these analyses also included the meteorological

data, but this was found to have little influence on cyanobacterial community composition

at any of the sites. In brief, the analyses found that both the environmental data and the

cyanobacterial community composition data at each of the three sites displayed

considerable variability in terms of both years and months, as also shown by the

previous analysis of the combined data for all sites. Comparisons of the environmental

data set with the cyanobacterial data set using RELATE for each site demonstrated a

very poor correlation between the two.

4 Discussion

The planktonic cyanobacteria in the northern (Québec) half of Missisquoi Bay have

displayed considerable temporal and spatial diversity during the period 2000 until 2008,

illustrated in particular by Figures 2 to 5 and confirmed by the PERMANOVA and

SIMPER analyses. Generally sites a and d8 on the western side of Missisquoi Bay had

a lower cyanobacterial presence than sites b and d2 on the eastern side of the bay.

Likewise, although some variation in the data may be due to differences in sampling

effort between years, there were nevertheless considerable annual differences.

Cyanobacterial were present at particularly high cell counts and biomass at most sites in

2001, 2002, 2003, 2004 and 2005, while cell counts and biomass were low at all 4 sites

sampled in 2007. Only site a was sampled in 2000, and although this site had high cell

counts, these were predominantly contributed by small Aphanothece clathrata so

biomass that year at that site was low. In 2006 sites a and d2 had considerable

cyanobacterial presence, whereas sites b and d8 had only a low to moderate

cyanobacterial presence. Similarly in 2008, site b recorded a considerably higher

cyanobacterial presence compared to sites a and d8. In most years the peak blooms did

not develop until later in summer, occurring in late July, August and September.

Microcystis was the dominant genus present at most sites in the major bloom years of

2001 to 2005, although the species did vary from year to year. Microcystis sp. was often

recorded as the major taxa present in 2001 and 2003, whereas M. viridus was dominant

at all four sites in 2004, and M. aeruginosa in 2005 and also in 2008. Microcystis has

also been recorded as the major cyanobacterial genus occurring in Missisquoi Bay prior

to 2006 by a number of other authors (Watzin et al., 2006; Mihuc et al., 2006; Boyer,

2008; Wheeler et al., 2012) who sampled mainly the American portion of the bay, but

who may have sometimes also sampled in Québec. Taxa comprising the cyanobacterial

communities in 2006 and 2007 have been more mixed. A later summer bloom of

Aphanizomenon flos-aquae occurred in 2006, whereas Anabaena flos-aquae and A.

spiroides occurred in 2006 and 2007. Correlation analysis indicated that a number of



cyanobacterial species were significantly positively correlated, forming mixed blooms in

Missisquoi Bay together.

Non-toxic species such as Aphanothece clathrata and Chroococcus dispersus were

sometimes dominant at some sites. Fortin et al. (2010) and McQuaid et al. (2011)

reported considerable variation in the cyanobacterial community in the north-east portion

of the bay near Saint-Armand occurring in 2006, 2007, and 2008 and they also reported

low cyanobacterial abundance in 2007. Likewise Davis et al. (2009) found that

Microcystis never numerically dominated at their sampling site in Missisquoi Bay in 2006,

with Anabaena dominating in late summer. Zamyadi et al. (2012) found Microcystis

aeruginosa dominant in the north-east of the bay in 2008, with other cyanobacterial taxa

as subdominants. Levine et al. (2012) reported that there were varying combinations of

Aphanizomenon, Anabaena, Gloeotrichia, Microcystis and Woronichinia occurring in

Missisquoi Bay since 2006, with Aphanizomenon dominating. In addition, Pearce et al.

(2013) have reported that Microcystis has become less dominant in summer blooms in

Missisquoi Bay in recent years, and suggested that the phytoplankton community may

be changing due to processes such as global warming or invasive species introductions.

This spatial and temporal variation in the cyanobacterial communities of Missisquoi Bay

was also highlighted by the multivariate analysis of the community data, with

PERMANOVA in particular showing significant differences between sites, between years

and between months. SIMPER analysis also showed considerable within-site, within-

year and within-month heterogeneity within the cyanobacterial community. Satellite

images of blooms in Missisquoi Bay from 2004 (Wheeler et al., 2012), and cruise data

from 2006 (Mihac et al., 2008) have also illustrated the large temporal and spatial

variability of cyanobacterial blooms and their community composition across the bay.

Cyanobacterial blooms in Missisquoi Bay are generally preceded in spring by eukaryotic

communities dominated by diatoms and cryptophytes, although the biomass of these

communities is usually low. Exponential growth in summer can lead to cyanobacteria at

times contributing to more than 80% of the total phytoplankton biomass present. As the

cyanobacterial blooms decline in late autumn, they are replaced again by diatoms and

cryptophytes. In 2007 when major cyanobacterial blooms did not develop, green algae

(Chlorophyceae) and the dinoflagellate Peridinium became dominant components of the

phytoplankton community. Correlation analysis indicated no significant correlation

between total cyanobacterial biomass and total eukaryotic biomass. Mihuc et al. (2005)

have reported a spring dominance of diatoms in Lake Champlain that were followed by

later summer shifts to Microcystis in the shallow waters of Missisquoi Bay. McQuaid et

al. (2011) reported that diatoms became dominant at end of September 2008.

The cyanotoxin microcystin was recorded in Missisquoi Bay at most sites each year from

2001 when toxin analysis commenced, apart from 2007 when all measurements were

below the level of detection and total cyanobacterial biomass was low. Microcystin-LR

was the most commonly occurring cyanotoxin, but microcystin-YR and microcystin-RR

also frequently co-occurred and increased in unison with microcystin-LR, and

occasionally concentrations of these two microcystin variants even exceeded those of

microcystin-LR. Watzin et al. (2006) and Boyer (2008) also reported that microcystin

was commonly detected in samples from the American side of Missisquoi Bay between

2000 and 2004. Fortin et al. (2010) reported very low microcystin concentrations in

2007, and that microcystin-LA was the predominant microcystin encountered in 2006.

Davis et al. (2009) also reported microcystins present in 2006.



Spearman Rank Order Correlation analyses indicated that total microcystin

concentrations in Missisquoi Bay were significantly positively correlated with total

Microcystis biomass, as well as with Microcystis aeruginosa and Microcystis sp.

biomass, as were the separate variants (with the exception of microcystin-YR and

Microcystis sp.). With some exceptions, microcystin concentrations were low when

species other than Microcystis dominated the cyanobacterial community. Fortin et al.

(2010) also found that Microcystis was the principal cyanobacterium producing

microcystin in Missisquoi Bay during their study, although Ngwa et al. (2014) reported a

substantial spatial variability in the proportion of toxic to non-toxic genotypes (2 to 23%)

of Microcystis at Québec sites in Missisquoi Bay in 2009, with non-toxic strains

dominating. Davis et al. (2009) found a significant positive correlation between

microcystin concentrations and densities of toxic Microcystis cells determined by

Polymerase Chain Reaction (PCR). Elsewhere, Rinta-Kanto et al. (2009) have also

found significant positive correlations between microcystin concentrations and

Microcystis abundance determined by PCR in Lake Erie, and Wood et al. (2012) have

found that high cell densities of Microcystis may enhance microcystin production. In

comparison, Watzin et al. (2006) reported that microcystin concentrations in other parts

of Lake Champlain in the USA were not correlated with cell densities, especially late in

the bloom period when cells may die and lyse, releasing the microcystin in soluble form

into the water column. In addition, Microcystis also appears to be able to switch

microcystin production on and off leading to large and rapid (within hours) increases and

decreases in microcystin concentrations in blooms (Wood et al., 2010). Such short-term

fluctuations would also affect correlations found between Microcystis abundance and

microcystin concentrations. Sinang et al. (2013) reported that the temporal variability of

total microcystin concentrations was up to four times greater than the temporal variability

of total cyanobacterial biomass in Western Australian lakes, and that the relationship

between the two was weak and site specific.

For the Québec portion of Missisquoi Bay, anatoxin-a was detected only once in this

study. However Boyer (2008) has reported animal fatalities due to anatoxin-a from

“northern regions of Lake Champlain” (not necessarily Missisquoi Bay), and that peaks in

anatoxin did not coincide with large microcystin-producing cyanobacterial blooms, but

occurred when significant concentrations of Anabaena spp. were present. This species

could be an anatoxin producer. In regards to other cyanotoxins, to our knowledge

saxitoxins and neosaxitoxins have never been measured in the Québec portion of

Missisquoi Bay, although this may perhaps be because appropriate standards to analyse

them have not been available. Zamyadi et al. (2012) did however detect traces of

cylindrospermopsin once in samples collected from near Saint-Armand in 2010.

Microcystin concentrations are likely to vary depending on environmental factors in much

the same way as cyanobacterial blooms vary with the environment, although Sinang et

al. (2013) consider that different environmental factors may influence each. In

Missisquoi Bay, total microcystin concentrations and concentrations of microcystin-LR,

microcystin-RR and microcystin-YR were significantly correlated with a number of

environmental variables (Appendix E). The correlation with chlorophyll-a may be due to

increased toxin concentrations with increased cyanobacterial biomass. Rinta-Kanto et

al. (2009) also found a significant positive correlation between total microcystin

(determined by Protein Phosphatase Inhibition analysis) and chlorophyll-a

concentrations in Lake Erie. Microcystin-RR and microcystin-YR in Missisquoi Bay were

positively correlated with water temperature, although total microcystin concentrations

were not.



Davis et al. (2009) found that the maximum cellular toxin content of Microcystis was

achieved at water temperatures of 25 to 30°C, their optimal growth temperature range.

Increasing turbidity and decreasing euphotic depth would result in lower light intensities

experienced by cyanobacteria entrained within the water column, while pH can influence

CO2 availability. Neilan et al. (2013) suggest that differences in light conditions, iron

availability, and CO2 concentrations may influence cyanobacterial blooms at a

community level as variations in the ratio of toxic to non-toxic genotypes may vary

according to these factors. This in turn would affect microcystin production by that

community.

Total microcystin and microcystin-LR and microcystin-YR concentrations in Missisquoi

Bay were also significantly positively correlated with total phosphorus concentrations,

and significantly negatively correlated with the TN:TP ratio. This probably results from

the significant positive correlations between many of the cyanobacterial taxa and total

phosphorus and the significant negative correlations with the TN:TP ratio, including

microcystin producing taxa; that is, increasing cyanobacterial biomass due to these

nutrient conditions was also reflected by higher microcystin concentrations. Rinta-Kanto

et al. (2009) reported similar results for Lake Erie, while Wilhelm et al. (2011) reported

only weak relationships between microcystin concentrations and nutrients in Taihu, a

large lake in eastern China, and no other strong relationships between toxin

concentrations and other environmental variables. Sinang et al. (2013) found that

relationships betwewen microcystin concentrations and total phosphorus varied

considerably from lake to lake and may be site specific. However Neilan et al. (2013)

point out that while eutrophication may stimulate toxic bloom development, it is currently

not known if phosphorus and nitrogen have direct effects on toxin production or if they

regulate the presence toxic and non-toxic genotypes, although they may stimulate

overall bloom community composition.

The very heterogeneous composition of the cyanobacterial community within Missisquoi

Bay, both spatially and temporally, may result partly from the heterogeneous nature of

water quality across the bay (Table 2). Multivariate analysis of the environmental data

using PCA and PERMANOVA also indicated significant spatial and temporal differences

in water quality. In particular sites b and d2 on the eastern side of Missisquoi Bay tend

to have generally higher TP concentrations, pH and turbidity and lower Secchi disc

depths compared to sites a and d8 in the centre and west of the bay. Both sites b and

d2, and site b in particular, are close to where the Pike River (Rivière aux Brochets)

discharges into Missisquoi Bay. The Pike River catchment is intensely agricultural with

considerable increases in the area of land cultivated for corn over the past two decades,

and it subsequently has high annual phosphorus and nitrogen yields and P and N

concentrations (Medalie et al., 2012). Moreover, the major source of nutrients in

Missisquoi Bay is the Missisquoi River (Smeltzer and Simoneau, 2008). A hydraulic

simulation confirmed by the results of a sampling program designed as a follow-up study

of the use of a lampricide in this river have shown that it discharges from its outlet in the

south of the bay towards Saint-Armand (Crowley et al., 2009). The nutrients contributed

by these rivers may partly account for the higher abundance of cyanobacteria measured

at these eastern sites (b and d2) during much of the 2001 – 2009 monitoring period. The

heterogeneous distribution of cyanobacterial blooms, including their community

composition, has been noted previously by Mihuc et al. (2008), and by Wheeler et al.

(2012).

Overall, Missisquoi Bay is eutrophic in terms of total phosphorus concentrations (Table

2), as has been described by several other authors (McQuaid et al., 2011; Zamyadi et



al., 2012; Levine et al., 2012; Smeltzer et al., 2012). McCarthy et al. (2013) consider

that nutrients probably do not limit primary production in the bay, although Davis et al.

(2009) reported that increased nitrogen stimulated increased growth rate of toxic

Microcystis at their sampling locations. Spearman Rank Order Correlation analysis

indicated that the biomass of many taxa of cyanobacteria, as well as total cyanobacterial

biomass were significantly positively correlated with total phosphorus concentrations,

both overall across all four sites (a, b, d2, d8) and often on a site by site basis, especially

at sites b and d2.

In comparison, total nitrogen (TN) concentrations recorded for the four sites were low,

even at site b (Table 3). The general trend is for TN and oxidised nitrogen (NOx)

concentrations to decrease over the summer period (June to August), then increase

once more in September, while TP concentrations show an opposite trend (data not

shown). McCarthy et al. (2013) also reported this trend with nitrogen. Spearman Rank

Order Correlation analysis showed few significant correlations between cyanobacterial

taxa and total nitrogen concentrations when data from all sites were combined, although

a number of taxa were significantly negatively correlated with NOx (Table F1, Appendix

F). On a site to site basis, there were significant negative correlations between the

biomass of several cyanobacterial taxa and NOx at sites b and d2 (Table F2, Appendix

F). This indicates the possible uptake of NOx by the cyanobacteria. The TN:TP ratios

(by concentration) were frequently less than 7.0 and are also indicative of possible

nitrogen limitation (or phosphorus excess). Decreasing nitrogen concentrations occurred

when heterocystis cyanobacterial species such as Anabaena spp. and Aphanizomenon

flos-aquae, which can overcome nitrogen limitation by fixing atmospheric nitrogen, were

present. However non-nitrogen fixing species such as Microcystis spp. were at times

also present when nitrogen concentrations were low, especially at site b. McCarthy et al.

(2013) reported few heterocytes being present in heterocystis species of Nostocales in

Missisquoi Bay, suggesting that cyanobacterial fixation of atmospheric nitrogen does not

contribute significantly to the overall nitrogen budget of the bay.

Total nitrogen concentrations in the Pike River (and in the Missisquoi River) appear to be

much higher (Medalie et al., 2012) than those in Missisquoi Bay (Table 2), even at sites

close to their inflow points, and McCarthy et al. (2013) have reported a similar spatial

variation. This suggests a rapid loss of nitrogen once inflows enter Missisquoi Bay in

summer, either by sedimentation or by denitrification. Denitrification is microbially

mediated and occurs more rapidly at warmer summer temperatures McCarthy et al.

(2013) also found that although inputs from tributaries contributed to the nitrogen

available for primary production within Missisquoi Bay, regeneration of autochthonous

organic material within the lake was a more important source of nitrogen for primary

productivity. Nevertheless, McCarthy et al. (2013) considered that neither tributary

inputs nor within-lake regeneration were sufficient to account for all the nitrogen demand

and therefore primary producers must supplement their nitrogen requirements from other

sources, including from the sediments. Pearce et al. (2013) report brief periods of

stratification and anoxic layers overlying the sediment-water interface that would allow

the diffusion of ammonium from the sediments that could be utilised by cyanobacteria.

Spearman Rank Order Correlation analysis also indicated some relationships between

cyanobacterial biomass and several other environmental attributes, such as dissolved

organic carbon (DOC), pH, turbidity and Secchi disc depth. Increasing DOC with

increasing cyanobacterial presence could be due to the production of soluble

metabolites by the cyanobacteria that are then released into the water. For example,

microcystins may be released into the water column from cells, especially if they die and



the cells lyse (Watzin et al., 2006). Correlations with pH, turbidity and Secchi disc depth

may be due to effects of the cyanobacteria themselves. Photosynthesis by

cyanobacteria during bloom periods removes most soluble CO2 from the water column

during daylight hours and results in an increase in pH (Paerl and Paul, 2012). The

presence of large amounts of cyanobacterial biomass can substantially increase the

turbidity of the water column, and at the same time reduce Secchi disc depth. These

interactions would lead to significant correlations between cyanobacteria and these

environmental attributes – positive in the case of pH and turbidity, and negative in the

case of Secch disc depth. These interactions also occurred at all four sites across the

bay, regardless of between site differences in these water quality variables.

Several taxa of cyanobacteria, especially Anabaena spp., were also positively correlated

with surface water temperature. Cyanobacteria are generally considered to grow better

at higher temperatures, especially above 25ºC (Elliott, 2012), and Davis et al. (2009)

have noted that blooms at their study sites on Missisquoi Bay corresponded to a

maximum water temperature of 26ºC. Likewise Mihuc et al. (2008) considered that high

abundance of cyanobacteria would only occur in Missisquoi Bay once temperature

thresholds of 25.5ºC were exceeded in late July, or thresholds of 21.5ºC were exceeded

in late August. Higher summer water temperatures (>25ºC) may support dominance by

Microcystis species as these have higher optimal growth rate temperatures (Carey et al.,

2012) than Anabaena or Aphanizomenon species, which may be an underlying factor for

some of the seasonal changes in cyanobacterial community composition observed in

this study and by others (Davis et al., 2009; Fortin et al., 2010).

There were few significant correlations between cyanobacterial presence and the

meteorological data from Philipsburg, although warm calm sunny conditions are

expected to enhance cyanobacterial presence, while cloudy wet cold and windy

conditions may reduce cyanobacterial presence. Higher maximum and minimum air

temperatures were sometimes correlated with a number of cyanobacterial taxa , and

especially when the 4, 7 or 10 day antecedent period was considered, indicating that a

week or more of hotter weather may benefit cyanobacteria in Missisquoi Bay in summer.

Wind direction was rarely correlated with cyanobacterial presence, and wind conditions

on the day of sampling had little effect on cyanobacterial distribution. Longer periods of

strong winds (10 days) may reduce Anabaena presence. Prolonged rain and cloud

cover may also reduce cyanobacterial presence, but this was not demonstrated

conclusively in this study. Investigations of the effects of annual variations in

meteorological conditions on cyanobacterial presence using site by site multivariate

analysis were also generally inconclusive, although possibly higher rainfall, increased

cloud cover and more southerly winds may have reduced cyanobacterial presence in

2007 and 2008. Fortin et al. (2010) considered that the predominant south-west winds

may lead to onshore accumulations of cyanobacterial scum near Saint-Armand, and also

suggested that rainfall may play a role in cyanobacterial bloom dynamics in the bay.

McQuaid et al. (2011) reported maximum cyanobacterial abundance during calm

conditions, while Wheeler et al. (2012) suggested that scums may form along the

eastern shoreline of the bay, but cyanobacteria would be mixed and dispersed down the

water column by wind on the western side. Mihuc et al. (2008) also suggested that wind

patterns and water currents would influence cyanobacterial bloom distribution around

Missisquoi Bay.

The multivariate analysis of the data tended to differ somewhat from the Spearman Rank

Order Correlation analysis. The multivariate analyses once again illustrate the

considerable heterogeneity in cyanobacterial bloom distribution and community



composition within Missisquoi Bay, both on a spatial and annual basis, and also on a

seasonal basis. The multivariate analysis of all four sites combined found a significant

positive correlation between the cyanobacterial community data and the environmental

data. Although total nitrogen concentrations, pH and surface water temperature were

suggested as the water quality attributes having the most effect on cyanobacterial

community composition, the relationships were not strong enough to identify these as

being the major drivers. Likewise total phosphorus was not indicated as a major driver

of cyanobacetrial presence, and there was a lot of unexplained variance as well. Site by

site analysis also displayed the heterogeneous temporal nature of much of the data, with

no significant correlations between the environmental data and the cyanobacterial

community data indicated for sites a and d8. Significant correlations between the

environmental and cyanobacterial data were only found for site b, where total nitrogen

concentrations, the TN:TP ratio (by concentration) and surface water temperature were

the environmental variables most correlated with the cyanobacterial community data.

Any relationships between cyanobacterial biomass and pigment concentrations in

Missisquoi Bay were also examined using Spearman Rank Order Correlation analysis.

Total cyanobacterial biomass and the biomass of most of the individual taxa or

groupings of cyanobacteria were significantly positively correlated with chlorophyll-a, and

also many with phycocyanin. This indicates that these two pigments are good indicators

of cyanobacterial presence within Missisquoi Bay in summer when cyanobacteria

dominate the phytoplankton, and especially of total cyanobacterial biomass. McQuaid et

al. (2011) has also found a significant positive relationship between phycocyanin

fluorescence measured in-situ in Missisquoi Bay and cyanobacterial biovolume, as have

Brient et al. (2008) and Bowling et al. (2013) elsewhere in the world.

Cyanobacterial blooms in Missisquoi Bay tend to occur in summer, and dominate the

phytoplankton community, contributing more than 70% of the total phytoplankton

biomass at times in July, August and September most years, except in 2000 and 2007.

In contrast, eukaryotic algae, especially diatoms and Cryptophyceae, tend to dominate

the phytoplankton community prior to the onset of the cyanobacterial blooms in spring,

and following the decline of the blooms in autumn, albeit at much lower total biomass.

Chlorophyceae and dinoflagellates also occur, but less frequently than the diatoms and

Cryptophyceae. Similar successional patterns have been reported in Missisquoi Bay by

Mihuc et al. (2005, 2008) and Levine et al. (2012), especially spring diatom abundance.

Spearman Rank Order Correlation analysis of major eukaryotic algal species with

environmental data indicated some significant correlations, but generally fewer than the

corresponding analyses with the cyanobacterial taxa. Significant correlations occurred

between several eukaryotic algal species and total phosphorus and especially with pH,

and surface water temperature, with the eukaryotic algae and cyanobacteria generally

showing similar responses to environmental cues in Missisquoi Bay. Carey et al. (2012)

have described the eco-physiological adaptations that cyanobacteria possess that may

give them a competative advantage over eukaryotic phytoplankton at warmer water

temperatures, and possibly such factors are supporting cyanobacterial abundance and

dominance during summer in Missisquoi Bay. There was usually a marked seasonal

succession pattern within the phytoplankton community each year with a spring

dominance of diatoms and a summer dominance of the cyanobacteria.

The multivariate analysis of the environmental and cyanobacterial data revealed only

weak linkages between the two data sets, and suggested that there were many



unexplained factors causing the heterogeneity shown by the cyanobacterial communities

in Missisquoi Bay. Several other environmental and biological factors not measured in

our study could interact to generate further variation in the cyanobacterial communities.

For example, increased concentrations of agricultural herbicides such as atrazine have

been proposed as one possible cause by Zananski et al. (2010), as this may kill off some

diatom species, but these authors were unable to conclude that cyanobacteria were

more resistant to herbicides than eukaryotic groups of phytoplankton and thus have a

competitive advantage. One biotic factor that has been proposed to have had major

impacts on the phytoplankton community and cyanobacterial presence is the invasion of

exotic species into Lake Champlain such as the zebra mussel (Dreissena polymorpha),

first reported from the lake in 1996 (Mihuc et al., 2005; 2006; 2012). Selective filtering

by zebra mussels is thought to remove many species of phytoplankton, while the larger

colonial forming Microcystis are not ingested, leading to their dominance (Mihuc et al.

2005; 2006). Smeltzer et al. (2012) however point out that Missisquoi Bay hosts a

relatively small population of zebra mussels, so their impacts on the phytoplankton

community may be less severe here than in other parts of Lake Champlain. An exotic

zooplanktovorious fish, the alewife (Alosa pseudoharengus) was also first discovered in

Missisquoi Bay in 2003 (Mihuc et al., 2012). Alewifes are size selective predators of

zooplankton in Lake Champlain, removing the larger sized species. Loss of the larger

zooplankton would reduce grazing pressure on phytoplankton, including cyanobacteria,

and could potentially lead to enhanced blooms (Mihuc et al., 2012), including in

Missisquoi Bay where nutrient concentrations and other environmental factors are

already favouring cyanobacterial blooms.

5 Conclusions

Analysis of the long term (2000 to 2008) phytoplankton and environmental data for

Missisquoi Bay collected by the MDDEFP has highlighted the following:-

Cyanobacterial blooms within the bay are diverse and highly variable in terms of both

abundance and composition, both between sites, within sites and between years and

seasons.

This heterogeneity in cyanobacterial bloom occurrence is apparent despite some

limitations encountered with the data, in particular the different sampling effort

maintained at different sampling sites and in different years.

Major cyanobacterial blooms occurred in Missisquoi Bay from 2001 until 2006, and

again in 2008. Microcystis was the dominant genus present in the bay prior to 2006,

with more mixed cyanobacterial communities occurring from 2006 onwards that

included both Aphanizomenon flos-aquae and Anabaena flos-aquae.

A general successional pattern was apparent in the phytoplankton community, with

diatoms and cryptophytes occurring in cooler periods in spring and autumn, and

cyanobacterial blooms confined to the warmer summer periods.

The cyanotoxin microcystin was recorded most years, with microcystin-LR as the

most common variant present compared to the two other variants analysed

(microcystin-RR and microcystin-YR). Because only three of the many variants of

microcystin were analysed, the data may underestimate total microcystin

concentrations as demonstrated by results for other variants measured in the bay

since 2009 (data not shown). Nevertheless microcystin concentrations were highly



correlated with Microcystis biomass, and were only present occasionally when other

cyanobacterial genera dominated. Factors that have been associated with increased

microcystin production include higher surface water temperature, and increased total

phosphorus, pH and turbidity (reduced light).

Principal Components Analysis (PCA) of the environmental data indicates that water

quality varies considerably at different locations across the bay, especially total

phosphorus, turbidity and pH which are higher on the eastern side compared to the

western side, and shallower Secchi disc depths also occur in the east. This

heterogeneous water quality distribution may be one cause of the high variability in

the cyanobacterial community composition and abundance.

Overall, Missisquoi Bay is eutrophic, and total phosphorus concentrations are a

major factor behind the cyanobacterial blooms.

Total nitrogen concentrations are often not high, and tend to decrease during

summer. Loss by sedimentation or bacteriologically mediated denitrification could be

possible causes. Uptake of soluble oxidized nitrogen by cyanobacteria could also be

occurring.

Cyanobacterial blooms also tend to occur at low TN:TP ratios, suggesting nitrogen

limitation, or excess phosphorus, during bloom periods. Input of nitrogen from

catchment sources would contribute to bloom development, despite low ambient

concentrations in the lake. In-situ nitrogen regeneration and inputs from other

sources have also been suggested in the literature as important sources of nitrogen.

The cyanobacteria themselves change some environmental conditions within the

lake, including increasing pH through use of carbon dioxide during photosynthesis.

Likewise the presence of their cells and colonies increases turbidity and reduces

Secchi disc depth. Their release of metabolites such as extracellular toxins may

increase DOC concentrations, although DOC increases may also be due to other

bacterial activity.

Spearman Rank Order Correlation analysis found few significant correlations with

meteorological factors, although 7 to 10 day periods of hotter weather may benefit

cyanobacteria in Missisquoi Bay in summer.

Spearman Rank Order Correlation analysis found cyanobacterial biomass was

strongly positively correlated with pigment concentrations (both chlorophyll-a and

phycocyanin), and these may be good indicators of cyanobacterial abundance in

summer. Phycocyanin in particular is asssociated with cyanobacteria, while high

chlorophyll-a concentrations can indicate blooms of both cyanobacteria and

eukaryotic algae.

Multivariate analysis of the cyanobacterial and environmental data (RELATE, BEST)

indicated no strong relationships between the cyanobacterial community composition

and environmental attributes. It showed that total nitrogen, pH and surface water

temperature were the factors most associated with cyanobacterial abundance and

community composition. The analysis also indicated a lot of unexplained variance in

these data.

Other environmental and biological factors for which there were no data for analysis

for this report, including herbicides, zebra mussels and the alewife, have been

suggested by other authors as possible factors influencing cyanobacterial community

composition in Missisquoi Bay and elsewhere in Lake Champlain.



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Appendix A List of sampling sites in the Québec portion of Missisquoi Bay, their location, the number of samples collected at each (between 2000 and 20081), and the period of record

Site Location Total number of samples

Period of record

a Centre of bay – deepest point (4.3 metres), pelagic

43 24 May 2000 –6 November 2008

b North east corner of bay 47 21 June 2001 – 6 November 2008

d1 Eastern shore, 1.5 km north of frontier 3 26 July 2001 – 18 July 2007

d2 Eastern shore, at Saint-Armand 17 26 July 2001 – 22 July 2009

d3 Eastern shore north of Saint-Armand 1 (scum) 21 August 2001

d4 North end, Baie de Venise 1 21 August 2001

d5 East shore of Baie de Venise 1 21 August 2001

d6 North side of Saint-Armand jetty 15 (all scum) 19 September 2001 – 22 July 2008

d7 Near shore close to site b 1 (scum) 2 October 2001

d8 North end, Baie de Venise 38 16 July 2002 – 6 November 2008

d9 North east corner of bay, near site b 3 16 July 2002 – 17 June 2003

d10 Offshore at PhilipsburgSaint-Armand 5 16 July 2002 – 19 August 2002

d11 Shoreline, north shore, Baie de Venise 1 (scum) 30 July 2002

d12 Shoreline, north shore, Baie de Venise 1 (scum) 5 August 2002

d13 Shoreline, north east corner of Baie de Venise 3 (2 scum) 19 August 2002 – 9 August 2006

d14 Shoreline 1 km north of Saint-Armand wharf 2 (all scum) 17 July 2004 – 3 August 2005

d15 Shoreline, north shore, more than 1 km east of Pointe Jameson

3 (2 scum) 19 July 2004 – 24 July 2006

d16 Pelagic, north end of bay, 1.5 km east of southern point of Pointe Jameson

1 21 July 2004

d17 Shoreline, north shore, Baie de Venise 3 (1 scum, 2 surface)

26 July 2005 – 2 August 2006

d18

(shoreline)

Near boat ramp, north west corner of Baie de Venise

1 (scum) 26 July 2005

d19 Mouth of Rivière aux Brochets (Pike River) 1 6 September 2005

d20 0.5 km offshore, south east of bay, just north of international frontier

2 18 July 2006 – 9 August 2006

d21 Shoreline, north east corner of bay, near d7 1 (scum) 24 July 2006



d22 North west shoreline (south west corner of Baie de Venise)

1 (surface) 24 July 2006

d23 Shoreline, north west corner of Baie de Venise 1 (scum) 27 September 2006

d24 Shoreline, north west corner of Baie de Venise 1 (scum) 27 September 2006

d25 Pelagic, north end of bay, 0.5 km east of southern point of Pointe Jameson

1 (surface) 12 July 2007

1 4 samples from site a, 4 samples from site b, 1 sample from site d2, and 3 samples from site d8 collected

in 2009 were also included in some of the statistical analyses. This is indicated in the text where

appropriate.



Appendix B Analytical methodology used by CEAEQ

Attributes

Analytical Methodology

Ammoniacal nitrogen1 Automated colorimetric method with sodium salicylate

Oxidised nitrogen: nitrate and nitrite

1

nitrogen

Automated colorimetric method with hydrazine sulfate and CNT

Total nitrogen1 Automated colorimetric method with UV digestion, hydrazine sulfate reduction

and CNT dosage

Total phosphorus1

Dissolved and in suspension

Trace amounts

Automated colorimetric method with ammonium molybdate

Automatedic colorimetric method and procedures adapted to low-concentration and trace-level phosphorus

Dissolved organic carbon

1

Infrared detection

pH1 Automatic titration

Conductivity1 Automatic titration

Turbidity1 Nephelometry

Chlorophyll-a2 Fluorometric identification of chlorophyll-a

Phycocyanin2 Semi-quantitative in vivo fluorometric assay with:

Turner-Designs TD-700 laboratory fluorometer and P/N 10-305 phycocyanin optical kit (2005-2008)

TriOS probe (2009)


2

Identification, counting and estimate of cyanobacteria and algae biological volume. (Identification especially of cyanobacteria species and eukaryote types). An upright Leica microscope was used and, starting in August 2003, an inverted microscope.

Microcystins (MC) and anatoxin-a

3

Identification of cyanotoxins in surface water: liquid chromatography dosage coupled with a MS/MS type of mass spectrometer. Analysis of extracellular (free in water) and intracellular forms. MC variants analyzed: LR, YR, RR.

Neosaxitoxin, saxitoxin and cylindrospermopsin

4

Identification of cyanotoxins in surface water: direct liquid chromatography dosage injection coupled with a MS/MS type of mass spectrometer. Analysis of extracellular (free in water) and intracellular forms.

1 Dassylva, N. May 31 and June 5, 2012. Validation d'informations méthodologiques - Rapport

baie Missisquoi 2000-2009, ([email protected] ) [Email]


2 Bastien, C. May 17, 2012. Validation d'informations méthodologiques - Rapport baie Missisquoi

2000-2009, ([email protected] ) [Email]


3 Deblois, C. May 28, 2012 (a). Validation d'informations méthodologiques - Rapport baie

Missisquoi 2000-2009, ([email protected] ) [Email]


4 Deblois, C. November 11, 2012 (b). Comment vous citer dans l'équipe de réalisation? / rapport

Baie Missisquoi, ([email protected] ) [Email]











Appendix C Cyanobacterial taxa that were present at sites a, b, d2 and d8 for the entire sampling campaigns between 2000 and 2008, and their dominance

The dominant taxa are identified by the maximum biomass (mg m-3) they attained on at

least one date during each year:

5 000 x 20 000 20 000 x 50 000 ≥ 50 000

Table C1 Site a

Species or genus

Year

Number of samples

2000 2001 2002 2003 2004 2005 2006 2007 2008

5 3 4 7 5 6 5 5 3

Potentially toxic cyanobacteria

Anabaena flos-aquae √ √ √ √ √ √ √ √

Anabaena planctonica √ √

Anabaena spiroides √ √ √ √ √

Aphanizomenon flos-aquae √ √ √ √ √ √ √

Coelosphaerium kuetzingianum √ √

Microcystis sp. √ 1 √

1 √

1 √

1 √ √

Microcystis aeruginosa √ √ √

Microcystis flos-aquae √

Microcystis viridis √ √

Woronichiana naegeliana √

Number of potentially toxic species 3 3 4 4 4 6 6 4 3

Other cyanobacteria

Anabaena crassa 2 √ √

Aphanocapsa delicatissima √ √ √ √ √

Aphanocapsa elachista √ √ √ √

Aphanocapsa planctonica 3 √

Aphanothece bachmanii √ √

Aphanothece clathrata √

Aphanothece clathrata var.brevis √ √

Aphanothece smithii √

Chroococcus dispersus √ √ √ √ √ √

Chroococcus dispersus var. minor √ √

Chroococcus limneticus √ √ √ √

Chroococcus minutus √



Chroococcus sp. √

Merismopedia minima √

Merismopedia punctata √ √

Merismopedia tenuissima √ √

Oscillatoria limnetica √ √

Oscillatoria tenuis √

Phormidium mucicola √ √ √ √ √

Pseudanabaena limnetica √

Pseudanabaena mucicola √

Rhabdoderma lineare √

Number of other species 2 4 4 3 9 7 7 6 6

1 From 2000 to 2004, Microcystis aeruginosa and Microcystis flos-aquae have been considered as

Microcystis sp.

2 Formerly Anabaena spiroides var crassa.

3 Aphanocapsa plantonica is a synonym of Aphanocapsa elachista and its original name (basionym) is

Aphanocapsa planctonica var plantonica.

Table C2 Site b

Species or genus

Year

Number of samples

2001 2002 2003 2004 2005 2006 2007 2008

10 7 6 4 6 5 5 4


Anabaena flos-aquae √ √ √ √ √ √ √

Anabaena planctonica √

Anabaena spiroides √ √ √ √ √ √

Aphanizomenon flos-aquae √ √ √ √ √ √ √ √

Gloeotrichia echinulata √ √

Microcystis sp √ 1 √

1 √

1 √

1 √ √ √

Microcystis aeruginosa √ √

Microcystis flos-aquae √

Microcystis viridis √ √

Microcystis wesenbergii √

Planktrotrix aghardii √

Number of potentiallt toxic species 3 5 4 6 8 4 4 4



Species or genus

Year

Number of samples

2001 2002 2003 2004 2005 2006 2007 2008

10 7 6 4 6 5 5 4

Other cyanobacteria

Anabaena crassa 2 √ √ √ √ √

Aphanizomenon gracil √

Aphanocapsa delicatissima √ √ √

Aphanocapsa elachista √ √ √

Aphanocapsa planctonica 3 √



Aphanothece clathrata var.brevis √ √ √


Chroococcus dispersus √ √ √ √ √ √


Chroococcus limneticus √ √ √ √

Chroococcus minutus √

Lyngbya limnetica √

Lyngbya mucicola √

Merismopedia glauca √

Merismopedia punctata √ √ √

Merismopedia tenuissima √

Oscillatoria limnetica √ √


Oscillatoria utermoehlii √ √

Phormidium mucicola √ √ √ √ √ √

Planktolyngbya sp √


Number of other species 11 6 5 7 9 4 4 7


Microcystis sp.


3 Aphanocapsa plantonica is a synonym of Aphanocapsa elachista and its original name (basionym) is

Aphanocapsa planctonica var plantonica.



Table C3 Site d2

Species or genus

Year

Number of samples

2001 20023 2003

3 2004 2005

3 2006 2007 2008

9 0 0 4 0 1 2 1


Anabaena flos-aquae √ √ √ √ √

Anabaena spiroides √ √

Anabaena sp √

Aphanizomenon flos-aquae √ √ √

Gloeotrichia echinulata √


1 √ √

Microcystis aeruginosa √

Microcystis viridis √

Number of potentially toxic species 3 5 4 4 2

Other cyanobacteria

Anabaena crassa 2 √

Aphanizomenon gracil √

Aphanocapsa delicatissima √ √

Aphanocapsa elachista √


Aphanothece clathrata var. brevis √

Aphanothece microspopra √

Chroococcus dispersus √ √

Merismopedia punctata √

Oscillatoria utermoehlii √

Phormidium mucicola √ √ √


Number of other species 7 5 1 1 3


Microcystis sp.


3 Site not sampled in 2002, 2003 and 2005



Table C4 Site d8

Species or genus

Year

Number of samples

2002 2003 2004 2005 2006 2007 2008

7 6 5 6 5 5 4


Anabaena flos-aquae √ √ √ √ √ √

Anabaena planctonica √

Anabaena spiroides √ √ √ √

Aphanizomenon flos-aquae √ √ √ √ √ √ √

Coelosphaerium naegelianum √

Gloeotrichia echinulata √

Gomphosphaeria lacustris √


1 √

1 √ √ √ √

Microcystis aeruginosa √ √ √

Microcystis flos-aquae √ √

Microcystis viridis √

Planktrotrix aghardii √

Number of potentially toxic species 5 4 4 8 4 4 6

Other cyanobacteria

Anabaena crassa 2 √ √

Aphanocapsa delicatissima √ √ √ √ √

Aphanocapsa elachista √ √ √

Aphanocapsa sp √



Aphanothece clathrata var. brevis √ √

Aphanothece microspopra √


Chroococcus dispersus √ √ √ √ √


Chroococcus limneticus √ √

Chroococcus minutus √ √

Coelomoron sp √

Merismopedia minima √

Merismopedia punctata √ √ √

Merismopedia tenuissima √ √




Species or genus

Year

Number of samples

2002 2003 2004 2005 2006 2007 2008

7 6 5 6 5 5 4

Oscillatoria lauterbonii √ √

Oscillatoria utermoehlii √

Phormidium mucicola √ √ √ √ √


Pseudanabaena mucicola √

Number of other species

7 3 7 12 4 4 10


Microcystis sp.




Appendix D Spearman Rank Order Correlation coefficients (Rho) for interactions between various cyanobacterial and eukaryotic algal groupings and taxa

All data were log transformed (log(x)) and “zero data” (i.e. no observation for that taxa in

that sample) were excluded. Only those correlations that were significant at a probability

of 0.05 or less are shown (+ve correlations in green, -ve in red). Scatterplots showing

examples of the relationships where the correlation coefficients were strongest are also

shown in Figure 11 of the report. Number of observations (n) varies in all cases due to

the variable number of samples that each taxon was recorded in.

Note: No attempt was done to adjust for multiple statistical significance testing, therefore

some correlations presented below might be declared significant by chance alone.

Variable

Spearman Rank Order Correlations (Rho) MD pairwise deleted

Marked correlations are significant at p <.05

Total cyanobacterial

biomass

Total eukaryote biomass

Total phytoplankton

biomass

Anabaena flos-aquae biomass


Total phytoplankton biomass 0.78 0.51

Anabaena flos-aquae biomass 0.71 0.56

Anabaena spiroides biomass 0.64 0.51

Total Anabaena biomass 0.81 0.66 0.85

Aphanizomenon flos-aquae

biomass 0.74 0.24 0.72 0.40

Microcystis sp. biomass 0.81 0.68 0.33

Microcystis aeruginosa biomass 0.90 0.54 0.57

Microcystis flos-aquae biomass 0.90

Total Microcystis biomass 0.88 0.68 0.48

Chlorella biomass 0.41 0.27 0.35

Closterium biomass 0.90

Cryptomonas biomass -0.21 0.64 0.29 -0.26

Cyclotella biomass 0.40 0.35

Fragilaria biomass

Melosira biomass 0.49 0. 56 0.61

Pandorina biomass -0.90

Péridinium biomass 0.68 0.88

Planktosphaeria biomass 0.42 0.49

Rhodomonas biomass 0.37 0.23

Stephanodiscus biomass 0.32

Oocystis biomass 0.29



Variable



Total cyanobacterial

biomass


Total phytoplankton

biomass

Anabaena flos-aquae biomass

Scenedesmus biomass 0.50 0.37 0.56 0.45

Variable



Anabaena spiroides biomass

Total Anabaena biomass

Aphanizomenon flos-aquae biomass

Microcystis sp. biomass

Total Anabaena biomass 0.80

Aphanizomenon flos-aquae biomass

0.37 0.52

Microcystis sp. biomass 0.38 0.75

Microcystis aeruginosa biomass 0.59 0.43

Microcystis flos-aquae biomass

Total Microcystis biomass 0.49 0.59 0.90

Chlorella biomass 0.42

Closterium biomass

Cryptomonas biomass 0.26


Fragilaria biomass 0.78

Melosira biomass 0.25 0.44 0.43

Pandorina biomass

Péridinium biomass 0.90

Planktosphaeria biomass 0.90

Rhodomonas biomass 0.29

Stephanodiscus biomass 0.40 0.59

Oocystis biomass

Scenedesmus biomass 0.48 0.46

Variable



Microcystis aeruginosa

biomass


Total Microcystis biomass

Chlorella biomass


Total Microcystis biomass 0.99

Chlorella biomass 0.62

Closterium biomass



Variable




biomass


Total Microcystis biomass

Chlorella biomass

Cryptomonas biomass


Fragilaria biomass

Melosira biomass 0.36 0.60

Pandorina biomass

Péridinium biomass


Rhodomonas biomass

Stephanodiscus biomass 0.85 0.53

Oocystis biomass


Variable



Closterium biomass Cryptomonas biomass

Cyclotella biomass

Cryptomonas biomass

Cyclotella biomass

Fragilaria biomass

Melosira biomass 0.49

Pandorina biomass

Péridinium biomass 0.59


Rhodomonas biomass 0.42

Stephanodiscus biomass

Oocystis biomass

Scenedesmus biomass 0.35

Variable



Fragilaria biomass Melosira biomass Pandorina biomass

Melosira biomass 0.60

Pandorina biomass

Péridinium biomass

Planktosphaeria biomass



Variable



Fragilaria biomass Melosira biomass Pandorina biomass

Rhodomonas biomass


Oocystis biomass


Variable



Péridinium biomass


Rhodomonas biomass


Rhodomonas biomass

Stephanodiscus biomass 0.59

Oocystis biomass

Scenedesmus biomass 0.77

Variable


Oocystis biomass

Oocystis biomass

Scenedesmus biomass



Appendix E Scatter diagrams of total microcystin concentrations against various water quality attributes (sites a, b, d2 and d8 combined), and Spearman Rank Order Correlation coefficients (Rho)

Most diagrams are plotted on a logarithmic scale. Data at or below the level of detection

have been removed. Spearman Rank Order Correlation coefficients (Rho) and

probabilities (p) are listed below in Table E1 (+ve correlations in green, -ve in red).



Total Microcystin ~ Chlorophyll-a

1

10

100

1000

0.01 0.10 1.00 10.00 100.00

Total Microcystin (μg L-1

)

Ch

loro

ph

yll

- a (

mg

m-3

)

Total Microcystin ~ Phycocyanin

1

10

100

1000

10000

0.01 0.10 1.00 10.00 100.00


)

Ph

yc

oc

ya

nin

(μ

g L

-1)

Total Microcystin ~ Total Phosphorus

1

10

100

1000

0.01 0.10 1.00 10.00 100.00


)

To

tal

Ph

os

ph

oru

s (

μg

L-1

)

Total Microcystin ~ Total Nitrogen

0.1

1.0

10.0

0.01 0.10 1.00 10.00 100.00


)

To

tal

Nit

rog

en

(m

g L

-1)

Total Microcystin ~ Total Nitrogen:Total Phosphorus (by concentration)

1

10

100

0.01 0.10 1.00 10.00 100.00


)

To

tal

Nit

rog

en

:To

tal

Ph

os

ph

oru

s (

by

co

nc

en

tra

tio

n)

Total Microcystin ~ Total Nitrogen:Total Phosphorus (by molecular

equivalents)

1

10

100

1000

0.01 0.10 1.00 10.00 100.00


)

To

tal

Nit

rog

en

:To

tal

Ph

os

ph

oru

s (

by

mo

lec

ula

r e

qu

iva

len

ts)



Total Microcystin ~ pH

5.0

5.5

6.0

6.5

7.0

7.5

8.0

8.5

9.0

9.5

10.0

0.01 0.10 1.00 10.00 100.00


)

pH

Total Microcystin ~ Turbidity

1

10

100

0.01 0.10 1.00 10.00 100.00


)

Tu

rbid

ity

(N

TU

)

Total Microcystin ~ Surface Water Temperature

0

5

10

15

20

25

30

35

0.01 0.10 1.00 10.00 100.00


)

Su

rfa

ce

Wa

ter

Te

mp

era

ture

(°C

)

Total Microcystin ~ Euphotic Depth (Secchi *2.7)

0

1

2

3

4

5

6

7

8

0.01 0.10 1.00 10.00 100.00


)

Eu

ph

oti

c D

ep

th (

me

tre

s)

Total Microcystin ~ Dissolved Oxygen

0

2

4

6

8

10

12

14

0.01 0.10 1.00 10.00 100.00


)

Dis

so

lve

d O

xy

ge

n (

mg

L-1

)

Total Microcystin ~ Dissolved Organic Carbon

0

2

4

6

8

10

12

14

16

18

0.01 0.10 1.00 10.00 100.00


)

Dis

so

lve

d O

rga

nic

Ca

rbo

n (

mg

L-1

)

Total Microcystin ~ Electrical Conductivity

100

110

120

130

140

150

160

0.01 0.10 1.00 10.00 100.00


)

Ele

ctr

ical

Co

nd

ucti

vit

y (

μS

cm

-1)

Total Microcystin ~ Ammoniacal Nitrogen

0.001

0.010

0.100

1.000

0.01 0.10 1.00 10.00 100.00


)

Am

mo

nia

ca

l N

itro

ge

n (

mg

L-1

)



Total Microcystin ~ Oxidized Nitrogen

0.001

0.010

0.100

1.000

0.01 0.10 1.00 10.00 100.00


)

Ox

idiz

ed

Nit

rog

en

(m

g L

-1)

Table E1 For comparisons with the scatterplots presented above in this Appendix, Spearman Rank Order Correlation coefficients (Rho) and probabilities (p) (+ve correlations in green, -ve in red) are provided in this table

Attributes n Rho P1

Log Total microcystin ~ Log Chlorophyll-a 80 0.39 <.001

Log Total microcystin ~ Log Phycocyanin 30 0.04

Log Total microcystin ~ Log Total phosphorus 71 0.41 <.001

Log Total microcystis ~ Log Total nitrogen 73 -0.04

Log Total microcystin ~ Log TN:TP (by concentrations) 67 -0.29 .016

Log Total microcystin ~ Log TN:TP (by molar equivalents) 67 -0.29 .016

Log Total microcystin ~ Dissolved organic carbon 39 -0.22

Log Total microcystin ~ Electrical conductivity 15 -0.18

Log Total microcystin ~ pH 82 0.38 <.001

Log Total microcystin ~ Log Turbidity 74 0.53 <.001

Log Total microcystin ~ Log NH3 24 -0.10

Log Total microcystin ~ Log NOx 18 -0. 45

Log Total microcystin ~ Surface water temperature 82 0.20

Log Total microcystin ~ Euphotic depth 80 -0.38 <.001

Log Total microcystin ~ Dissolved oxygen 46 0.03

Log MC-LR ~ Log Chlorophyll-a 80 0.45 <.001

Log MC-LR ~ Log Phycocyanin 30 0.21

Log MC-LR ~ Log Total phosphorus 71 0.39 .001

Log MC-LR ~ Log Total nitrogen 73 0.01

Log MC-LR ~ Log TN:TP (by concentrations) 67 -0.23

Log MC-LR ~ Log TN:TP (by molar equivalents) 67 -0.23

Log MC-LR ~ Dissolved organic carbon 39 -0.20

Log MC-LR ~ Electrical conductivity 15 -0.18

Log MC-LR ~ pH 82 0.37 .001

Log MC-LR ~ Log Turbidity 74 0.50 <.001

Log MC-LR ~ Log NH3 24 -0.10



Log MC-LR ~ Log NOx 18 -0.56 .02

Log MC-LR ~ Surface water temperature 82 0.08

Log MC-LR ~ Euphotic depth 80 -0.38 <.001

Log MC-LR ~ Dissolved oxygen 46 0.12

Log MC-RR ~ Log Chlorophyll-a 45 0.33 .03

Log MC-RR ~ Log Phycocyanin 28 -0.19

Log MC-RR ~ Log Total phosphorus 38 0.14

Log MC-RR ~ Log Total nitrogen 39 -0.14

Log MC-RR ~ Log TN:TP (by concentrations) 34 -0.17

Log MC-RR ~ Log TN:TP (by molar equivalents) 34 -0.17

Log MC-RR ~ Dissolved organic carbon 8 -0.36

Log MC-RR ~ Electrical conductivity <3

Log MC-RR ~ pH 46 0.57 <.001

Log MC-RR ~ Log Turbidity 40 0.47 .002

Log MC-RR ~ Log NH3 9 -0.39

Log MC-RR ~ Log NOx 3 -0.50

Log MC-RR ~ Surface water temperature 45 0.43 .003

Log MC-RR ~ Euphotic depth 44 -0.40 .007

Log MC-RR ~ Dissolved oxygen 40 0.24

Log MC-YR ~ Log Chlorophyll-a 43 0.37 .014

Log MC-YR ~ Log Phycocyanin 25 -0.15

Log MC-YR ~ Log Total phosphorus 36 0.53 .001

Log MC-YR ~ Log Total nitrogen 37 -0.15

Log MC-YR ~ Log TN:TP (by concentrations) 32 -0.45 .009

Log MC-YR ~ Log TN:TP (by molar equivalents) 32 -0.45 .009

Log MC-YR ~ Dissolved organic carbon 8 0.19

Log MC-YR ~ Electrical conductivity <3

Log MC-YR ~ pH 45 0.60 <.001

Log MC-YR ~ Log Turbidity 37 0.60 <.001

Log MC-YR ~ Log NH3 9 0.15

Log MC-YR ~ Log NOx 3 -0.50

Log MC-YR ~ Surface water temperature 45 0.47 .001

Log MC-YR ~ Euphotic depth 44 -0.44 .003

Log MC-YR ~ Dissolved oxygen 40 -0.25

1 Only those correlations that were significant at or less than 0.05 are indicated here.



Appendix F Spearman Rank Order Correlation coefficients (Rho) for interactions between various cyanobacterial and eukaryotic algal groupings and taxa against physico-chemical variables

Only those correlations that were significant at a probability of 0.05 or less are shown

(+ve in green, -ve in red). Scatterplots showing examples of the relationships where the

correlation coefficients were strongest are also shown. The number of observations (n)

varied considerably as the number of data points for each attribute was not consistent,

and is therefore not provided for each correlation.



Table F1. Data from all 4 sites combined

Variable

1

Log Chla

2

Log PCY3 Log TP

4 Log TN

5 Log TN:TP

CYANOBACTERIA

Log Total cyanobacterial biomass 0.78 0.47 0.51 -0.22 -0.54

Log Total eukaryote biomass 0.28 0.21

Log Total Phytoplankton biomass 0.87 0.36 0.60 -0.51

Log Anabaena flos-aquae biomass 0.64 0.51 -0.52

Log Anabaena spiroides biomass 0.46 0.41

Log Total Anabaena biomass 0.72 0.36 0.37 -0.32

Log Aphanizomenon flos-aquae biomass 0.69 0.65 0.53 -0.40

Log Microcystis sp. biomass 0.69 0.87 0.53 -0.47

Log Microcystis aeruginosa biomass 0.56 0.58 0.41 -0.44

Log Total Microcystis biomass 0.71 0.59 -0.56

EUKARYOTIC ALGAE

Log Chlorella biomass 0.33

Log Cryptomonas biomass

Log Cyclotella biomass 0.33 0.23

Log Fragilaria biomass

Log Melosira biomass 0.48 0.25 -0.33 -0.38

Log Oocystis biomass 0.33 0.44

Log Péridinium biomass

Log Planktosphaeria Biomass 0.38

Log Rhodomonas biomass

Log Scenedesmus biomass 0.43 0.30 -0.28

Log Stephanodiscus biomass 0.27 0.61 0.27



Variable

1

DOC6 Cond

7 pH Log Turb

8

CYANOBACTERIA

Log Total cyanobacterial biomass 0.45 0.71 0.76

Log Total Phytoplankton biomass 0.45 0.61 0.70

Log Anabaena flos-aquae biomass 0.51 0.55 0. 61

Log Anabaena spiroides biomass 0.63 0.40 0.36

Log Total Anabaena biomass 0.72 0.61 0.64

Log Aphanizomenon flos-aquae biomass 0.42 0.46

Log Microcystis sp. biomass 0.51 0.58

Log Microcystis aeruginosa biomass 0.63 0.59

Log Total Microcystis biomass 0.63 0.67

EUKARYOTIC ALGAE

Log Chlorella biomass 0.58 0.47

Log Cryptomonas biomass

Log Cyclotella biomass 0.55

Log Fragilaria biomass 0.53

Log Melosira biomass 0.42 0.40

Log Oocystis biomass

Log Péridinium biomass

Log Planktosphaeria biomass 0.97

Log Rhodomonas biomass

Log Scenedesmus biomass 0.53 0.36

Log Stephanodiscus biomass 0.46

Variable

1

Log NH3

9

Log NOx

10

TEMP SURF

11

Zeu12

(from Secchi)

DO13

CYANOBACTERIA

Log Total cyanobacterial biomass -0.83 0.25 -0.65

Log Total eukaryote biomass -0.19

Log Total phytoplankton biomass -0.70 -0.70

Log Anabaena flos-aquae biomass -0.64 0.24 -0.57

Log Anabaena spiroides biomass 0.46 -0.38 0.7

Log Total Anabaena biomass -0.67 0.29 -0.66

Log Aphanizomenon flos-aquae biomass -0.63 -0.52 0.46



Variable

1

Log NH3

9

Log NOx

10

TEMP SURF

11

Zeu12

(from Secchi)

DO13

Log Microcystis sp. biomass -0.85 -0. 64

Log Microcystis aeruginosa biomass 0.70 -0.37

Log Total Microcystis biomass -0.85 0.21 -0.67

EUKARYOTIC ALGAE

Log Chlorella biomass 0.55

Log Cryptomonas biomass -0.19 0.28

Log Cyclotella biomass -0.43

Log Fragilaria biomass 0.72

Log Melosira biomass 0.32 -0.50

Log Oocystis biomass

Log Péridinium biomass 0.71

Log Planktosphaeria biomass 0.36

Log Rhodomonas biomass -0.35 -0.46

Log Scenedesmus biomass 0.47 -0.38

Log Stephanodiscus biomass 0.67

1 Microcystis flos-aquae, Closterium and Pandorina also tested, but as there were no significant correlations,

they are not included in the table. 2 Chla = Chlorophyll-a,

3 PCY = Phycocyanin,

4 TP = Total phosphorus,

5 TN = Total nitrogen,

6 DOC =

Dissolved organic carbon, 7 Cond = Electrical conductivity,

8 Turb = Turbidity,

9 NH3 = Ammoniacal nitrogen,

10 NOx = Oxidised nitrogen,

11 TEMP Surf = Surface water temperature,

12 Zeu – Euphotic depth,

13 DO =

Dissolved oxygen.

Chlorophyll-a ~ Total Cyanobacterial Biomass

1

10

100

1000

10000

100000

1000000

1.0 10.0 100.0 1000.0

Chlorophyll-a (mg m-3

)

To

tal

Cy

an

ob

ac

teri

al

Bio

ma

ss

(m

g m

-3)

Chlorophyll-a ~ Total Eukaryotic Algal Biomass

1

10

100

1000

10000

100000

1.0 10.0 100.0 1000.0


)

To

tal

Eu

ka

ryo

tic

Alg

al

Bio

ma

ss

(m

g m

-3)



Chlorophyll-a ~ Total Anabaena Biomass

1

10

100

1000

10000

100000

1000000

1.0 10.0 100.0 1000.0


)

To

tal

An

ab

ae

na

Bio

ma

ss

(m

g m

-3)

Chlorophyll-a ~ Aphanizomenon flos-aquae Biomass

1

10

100

1000

10000

100000

1000000

1.0 10.0 100.0 1000.0


)

Bio

ma

ss

of

Ap

ha

niz

om

en

on

flo

s-a

qu

ae

(m

g

m-3

)

Chlorophyll-a ~ Total Microcystis Biomass

1

10

100

1000

10000

100000

1.0 10.0 100.0 1000.0


)

To

tal

Mic

roc

ys

tis

Bio

ma

ss

(m

g m

-3)

Phycocyanin ~ Total Cyanobacterial Biomass

1

10

100

1000

10000

100000

1000000

1.0 10.0 100.0 1000.0 10000.0

Phycocyanin (mg m-3

)

To

tal

Cy

an

ob

ac

teri

al

Bio

ma

ss

(m

g m

-3)

Phycocyanin ~ Anabaena spiroides Biomass

1

10

100

1000

10000

100000

1000000

1 10 100 1000 10000

Phycocyanin (mg m-3

)

An

ab

ae

na

sp

iro

ide

s B

iom

as

s (

mg

m-3

)

Phycocyanin ~ Aphanizomenon flos-aquae Biomass

1

10

100

1000

10000

100000

1000000

1.0 10.0 100.0 1000.0 10000.0

Phycocyanin (mg m-3

)

Bio

ma

ss

of

Ap

ha

niz

om

en

on

flo

s-a

qu

ae

(m

g

m-3

)

Phycocyanin ~ Microcystis sp. Biomass

1

10

100

1000

10000

100000

1.0 10.0 100.0 1000.0 10000.0

Phycocyanin (mg m-3

)

Mic

roc

ys

tis

sp

. B

iom

as

s (

mg

m-3

)

Total Phosphorus ~ Total Cyanobacterial Biomass

1

10

100

1000

10000

100000

1000000

1 10 100 1000

Total Phosphorus (μg L-1

)

To

tal

Cy

an

ob

ac

teri

al

Bio

ma

ss

(m

g m

-3)



Total Phosphorus ~ Aphanizomenon flos-aquae Biomass

1

10

100

1000

10000

100000

1000000

1 10 100 1000


)

Bio

ma

ss

of

Ap

ha

niz

om

en

on

flo

s-a

qu

ae

(m

g

m-3

)

Total Phosphorus ~ Total Microcystis Biomass

1

10

100

1000

10000

100000

1 10 100 1000


)

To

tal

Mic

rocysti

s B

iom

ass (

mg

m-3

)

Total Nitrogen : Total Phosphous (by concentration) ~ Total Cyanobacterial

Biomass

1

10

100

1000

10000

100000

1000000

0.1 1 10 100

Total Nitrogen : Total Phosphorus (by concentration)

To

tal

Cy

an

ob

ac

teri

al

Bio

ma

ss

(m

g m

-3)

Total Nitrogen : Total Phosphorus (by concentration) ~ Anabaena flos-

aquae Biomass

1

10

100

1000

10000

100000

0.1 1 10 100


An

ab

ae

na

flo

s-a

qu

ae

Bio

ma

ss

(m

g m

-3)

Total Nitrogen : Total Phosphorus (by concentration) ~ Total Microcystis

Biomass

1

10

100

1000

10000

100000

0.1 1 10 100


To

tal

Mic

roc

ys

tis

Bio

ma

ss

(m

g m

-3)

Dissolved Organic Carbon ~ Total Cyanobacterial Biomass

1

10

100

1000

10000

100000

1000000

0 2 4 6 8 10 12 14 16 18

Dissolved Organic Carbon (mg L-1

)

To

tal

Cy

an

ob

ac

teri

al

Bio

ma

ss

(m

g m

-3)

pH ~ Total Cyanobacterial Biomass

1

10

100

1000

10000

100000

1000000

4 5 6 7 8 9 10 11

pH

To

tal

Cy

an

ob

ac

teri

al

Bio

ma

ss

(m

g m

-3)

pH ~ Total Anabaena Biomass

1

10

100

1000

10000

100000

1000000

4 5 6 7 8 9 10 11

pH

To

tal

An

ab

ae

na

Bio

ma

ss

(m

g m

-3)



pH ~ Aphanizomenon flos-aquae Biomass

1

10

100

1000

10000

100000

1000000

4 5 6 7 8 9 10 11

pH

Bio

ma

ss

of

Ap

ha

niz

om

en

on

flo

s-a

qu

ae

(m

g

m-3

)

pH ~ Total Microcystis Biomass

1

10

100

1000

10000

100000

4 5 6 7 8 9 10 11

pH

To

tal

Mic

roc

ys

tis

Bio

ma

ss

(m

g m

-3)

Turbidity ~ Total Cyanobacterial Biomass

1

10

100

1000

10000

100000

1000000

1 10 100

Turbidity (NTU)

To

tal

Cy

an

ob

ac

teri

al

Bio

ma

ss

(m

g m

-3)

Turbidity ~ Total Anabaena Biomass

1

10

100

1000

10000

100000

1000000

1.0 10.0 100.0

Turbidity (NTU)

To

tal

An

ab

ae

na

Bio

ma

ss

(m

g m

-3)

Turbidity ~ Aphanizomenon flos-aquae Biomass

1

10

100

1000

10000

100000

1000000

1 10 100

Turbidity (NTU)

Bio

ma

ss

of

Ap

ha

niz

om

en

on

flo

s-a

qu

ae

(m

g

m-3

)

Turbidity ~ Total Microcystis Biomass

1

10

100

1000

10000

100000

1 10 100

Turbidity (NTU)

To

tal

Mic

roc

ys

tis

Bio

ma

ss

(m

g m

-3)

Oxidized Nitrogen ~ Total Cyanobacterial Biomass

1

10

100

1000

10000

100000

1000000

0.01 0.1 1 10

Oxidized Nitrogen (mg L-1

)

To

tal

Cy

an

ob

ac

teri

al

Bio

ma

ss

(m

g m

-3)

Oxidized Nitrogen ~ Aphanizomenon flos-aquae Biomass

1

10

100

1000

10000

100000

1000000

0.01 0.1 1 10


)

Bio

ma

ss

of

Ap

ha

niz

om

en

on

flo

s-a

qu

ae

(m

g

m-3

)



Oxidized Nitrogen ~ Total Microcystis Biomass

1

10

100

1000

10000

100000

0.01 0.1 1 10


)

To

tal

Mic

roc

ys

tis

Bio

ma

ss

(m

g m

-3)

Surface Water Temperature ~ Total Anabaena Biomass

1

10

100

1000

10000

100000

1000000

0 5 10 15 20 25 30 35

Surface Water Temperature (oC)

To

tal

An

ab

aen

a B

iom

ass (

mg

m-3

)

Table F2. Summary of results of site by site Spearman Rank Order Correlation (Rho) analysis between log transformed phytoplankton biomass data and log transformed water quality data (except DOC, electrical conductivity, pH, temperature, Secchi depth and dissolved oxygen). Only those correlations that were significant at a probability of 0.05 or less are shown. n was very variable between analyses and is therefore not shown.

Note: No attempt was done to adjust for multiple statistical significance testing, therefore some correlations

presented below might be declared significant by chance alone.

Water quality variable1

Significant correlations positive (+) or negative (-) per taxa

Site

a b d2 d8

TP + Total phytoplankton

+ Total cyanobacteria

+ Anabaena flos-aquae

+ Aphanizomenon flos-aquae

+ Total Microcystis

+ Microcystis aeruginosa

+ Microcystis sp.

- Planktospaheria

TN - Total phytoplankton

- Total cyanobacteria

+ Anabaena spiroides

- Melosira

TN:TP - Total phytoplankton


- Anabaena flos-aquae


- Total Microcystis

- Microcystis aeruginosa

- Melosira

+ Planktosphaeria





Site

a b d2 d8

DOC + Total phytoplankton


+ Total Anabaena


+ Total Microcystis

+ Microcystis sp.

+ Chlorella

+ Cryptomonas

Electrical conductivity

- Cryptomonas

pH + Total phytoplankton


+ Total Anabaena



+ Total Microcystis

+ Microcystis sp.


+ Chlorella

+ Cyclotella

+ Melosira

+ Oocystis

+ Stephandiscus

Turbidity + Total phytoplankton


+ Total Anabaena



+ Total Microcystis

+ Microcystis sp.


- Cryptomonas

+ Melosira

+ Scenedesmus

NOx - Total phytoplankton






Site

a b d2 d8

- Aphanizomenon flos-aquae

- Total Microcystis

- Microcystis sp.

- Cryptomonas

Surface water temperature


+ Total Anabaena


+ Total Microcystis


+ Chlorella

+ Fragellaria

+ Melosira

+ Oocystis

+ Scenedesmus

Secchi depth - Total phytoplankton


- Total Anabaena

- Anabaena flos-aquae

- Aphanizomenon flos-aquae

- Total Microcystis

- Microcystis sp.

- Total eukaryotic algae

- Cyclotella

- Melosira

- Stephanodiscus

Dissolved oxygen + Anabaena spiroides

- Cyclotella

1 There were no significant correlations for NH3.



Appendix G Spearman Rank Order Correlation coefficients (Rho) and probabilities (p) for interactions between the biomass of various cyanobacterial and eukaryotic algal taxa (log(x) transformed) and meteorological variables measured at Philipsburg manual weather station

Only those correlations that were significant at or less than 0.05 are shown (+ve in green, -ve in red). Meteorological data are for the

day of sampling and the means of the 4 day, 7 day and 10 day antecedent period prior to sampling. BGA = blue-green algae

(cyanobacteria). Note: No attempt was done to adjust for multiple statistical significance testing, therefore some correlations

presented below might be declared significant by chance alone.

Maximum temperature

Site Total BGA Toxic BGA Total Eukaryotic

Algae

Total

phytoplankton

Anabaena flos-aquae

Anabaena spiroides

Total Anabaena



Microcystis sp.

Total Microcystis

Same day

a

b 0.48 -0.44

d2

d8 0.36 0.40 0.81

Combined -0.25 0.55

4 day antecedent

a 0.43

b 0.51 0.41

d2 0.80

d8

Combined 0.36 0.57 0.21



7 day antecedent

a 0.39 0.79 0.46

b 0.40 -0.45

d2 0.78

d8 0.41

Combined -0.25 0.25 0.28 -0.24 0.65 0.21

10 day antecedent

a 0.89 0.48

b -0.47

d2 0.64

d8

Combined -0.21 0.22 0.73



Maximum temperature

Site Chlorella Closterium

Cryptomonas

Cyclotella

Fragellaria

Melosira Mougeotia

Pandorina

Peridinium

Plankto sphaeria

Rhodomonas

Stephanodiscus

Oocystis Scenedesmus

Same day

a

b

d2

d8 0.78 0.50 0.66

Combined 0.63 0.29

4 day antecedent

a 0.47

b

d2

d8 0.51

Combined 0.38 0.36

7 day antecedent

a -0.50 0.63

b -0.39

d2

d8 0.51

Combined 0.34 -0.26 0.76 -0.28



10 day antecedent

a 0.46 -0.44 0.62

b -0.34

d2

d8 0.66 0.53 0.50

Combined 0.37 -0.21 0.76 -0.26



Minimum temperature


Algae

Total

phytoplankton

Anabaena flos-aquae

Anabaena spiroides

Total Anabaena



Microcystis sp.

Total Microcystis

Same day

a 0.79 0.62

b 0.72 0.62

d2

d8

Combined 0.27 0.56 0.33 0.58

4 day antecedent

a 0.55 0.50

b 0.68 0.41

d2 0.69

d8

Combined 0.34 0.37 0.30 0.47

7 day antecedent

a 0.43 0.79

b 0.78 0.38 -0.45

d2 0.59

d8

Combined -0.23 0.46 0.23 -0.27 0.60



10 day antecedent

a 0.89

b 0.70 0.38 -0.52

d2

d8

Combined -0.22 0.36 0.21 -0.30 0.68



Minimum temperature


Cryptomonas

Cyclotella

Fragellaria

Melosira Mougeotia

Pandorina

Peridinium

Plankto sphaeria

Rhodomonas

Stephanodiscus


Same day

a

b

d2

d8 0.55 0.65 0.62

Combined 0.33 0.68 0.28

4 day antecedent

a 0.51

b

d2

d8

Combined 0.31

7 day antecedent

a 0.57

b

d2

d8 0.47 0.75

Combined 0.34 0.50 0.76 -0.18



10 day antecedent

a 0.57

b

d2

d8 0.50

Combined 0.37 0.76 -0.21



Rain


Algae

Total

phytoplankton

Anabaena flos-aquae

Anabaena spiroides

Total Anabaena



Microcystis sp.

Total Microcystis

Same day

a -0.39

b 0.76

d2

d8 0.61

Combined -0.18 0.40 0.45

4 day antecedent

a

b 0.55

d2

d8

Combined 0.52

7 day antecedent

a

b

d2

d8

Combined -0.21 0.44



10 day antecedent

a

b -0.29

d2

d8 -0.34

Combined -0.26 -0.17



Rain


Cryptomonas

Cyclotella

Fragellaria

Melosira Mougeotia

Pandorina

Peridinium

Plankto sphaeria

Rhodomonas

Stephanodiscus


Same day

a 0.58

b

d2

d8 0.57

Combined 0.45

4 day antecedent

a 0.47 -0.52

b

d2

d8

Combined 0.79 0.90 -0.35

7 day antecedent

a

b

d2

d8

Combined 0.94 -0.18 -0.25



10 day antecedent

a

b

d2

d8 0.63

Combined 0.19 0.38



Wind direction


Algae

Total

phytoplankton

Anabaena flos-aquae

Anabaena spiroides

Total Anabaena



Microcystis sp.

Total Microcystis

Same day

a 0.40

b 0.77

d2

d8

Combined 0.33 0.57

4 day antecedent

a

b

d2

d8

Combined

7 day antecedent

a

b

d2

d8

Combined



10 day antecedent

a

b 0.40

d2

d8 0.79

Combined 0.46 -0.62



Wind Direction


Cryptomonas

Cyclotella

Fragellaria

Melosira Mougeotia

Pandorina

Peridinium

Plankto sphaeria

Rhodomonas

Stephanodiscus


Same day

a

b

d2 0.81

d8

Combined 0.63 -0.37

4 day antecedent

a

b

d2

d8

Combined -0.77

7 day antecedent

a 0.58

b

d2

d8

Combined 0.94



10 day antecedent

a

b 0.53

d2 0.51

d8

Combined 0.39



Wind velocity


Algae

Total

phytoplankton

Anabaena flos-aquae

Anabaena spiroides

Total Anabaena



Microcystis sp.

Total Microcystis

Same day

a

b

d2

d8

Combined

4 day antecedent

a 0.91

b

d2

d8

Combined 0.20 0.30 -0.67 -0.20

7 day antecedent

a 0.36 -0.79

b -0.31 -0.33

d2

d8

Combined -0.21 -0.25 0.24 -0.72 -0.29



10 day antecedent

a 0.47

b -0.47 -0.56 -0.54

d2 0.50

d8 -0.87 -0.41

Combined -0.26 -0.25 0.34 -0.28 -0.43 -0.36



Wind velocity


Cryptomonas

Cyclotella

Fragellaria

Melosira Mougeotia

Pandorina

Peridinium

Plankto sphaeria

Rhodomonas

Stephanodiscus


Same day

a

b -0.44

d2

d8

Combined

0.50 -0.25

4 day antecedent

a 0.37

b

d2

d8

Combined 0.21 0.71

7 day antecedent

a 0.47 -0.72

b 0.39 -0.71

d2

d8

Combined 0.27 0.28 -0.45



10 day antecedent

a

b -0.61

d2

d8 0.63

Combined 0.25 0.22 0.28



Cloud cover


Algae

Total

phytoplankton

Anabaena flos-aquae

Anabaena spiroides

Total Anabaena



Microcystis sp.

Total Microcystis

Same day

a

b -0.32

d2

d8

Combined -0.24 0.34 -

4 day antecedent

a

b -0.32 -0.36

d2

d8 -0.69

Combined -0.24 -0.24 -0.24

7 day antecedent

a

b -0.32

d2

d8 -0.65

Combined -0.22 -0.23 -0.21



10 day antecedent

a

b -0.34

d2 -0.69

d8 0.33

Combined -0.25



Cloud cover


Cryptomonas

Cyclotella

Fragellaria

Melosira Mougeotia

Pandorina

Peridinium

Plankto sphaeria

Rhodomonas

Stephanodiscus


Same day

a -0.42

b -0.36

d2

d8


4 day antecedent

a -0.43

b -0.41

d2

d8 0.71 -


7 day antecedent

a

b

d2

d8 0.70

Combined -0. 30



10 day antecedent

a

b

d2

d8 -0.50




Appendix H Output from the multivariate analyses

Transform: Log(X+1)

Resemblance: S17 Bray Curtis similarity

SiteSite a

Site b

Site d2

Site d8

2D Stress: 0.23

Transform: Log(X+1)


Year2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2D Stress: 0.23

Transform: Log(X+1)


MonthMay

June

August

September

October

July

November

2D Stress: 0.23

Transform: Log(X+1)


LocationWest

East

2D Stress: 0.23

Figure H1 nMDS output for the complete cyanobacterial data set, plotted for sites, years, months and location (east/west)

Table H1 Average within-site similarities and the major taxa contributing to this within-site similarity by more than 10% at each site on Missisquoi Bay, using the complete cyanobacterial data set

Sites Average within-site similarity (%)

Major contributors per site (with percent contribution in brackets)

Site a 22.3 Anabaena flos-aquae (24.9%), Aphanizomenon flos-aquae (14.3%), Microcystis sp. (14.1%), Phormidium mucicola (13.3%)

Site b 28.1 Aphanizomenon flos-aquae (26.2%), Microcystis sp. (20.0%), Anabaena flos aquae (14.5%), Chroococcus dispersus (11.6%)

Site d2 36.9 Microcystis sp. (36.3%), Anabaena flos aquae (24.5%), Aphanizomenon flos-aquae (20.8%)

Site d8 25.0 Microcystis sp. (22.3%), Phormidium mucicola (18.5%), Anabaena flos-aquae (18.2%), Chroococcus dispersus (13.6%)



Table H2 Average within-year similarities and the major taxa contributing to this within-year similarity by

more than 10% for each year at Missisquoi Bay, using the complete cyanobacterial data set

Year Average within-year similarity (%)

Major contributors per year (with percent contribution in brackets)

2000 68.6 Aphanothece spp. (47.0%), Coelosphaerium kuetzingianum (44.2%),

2001 43.6 Microcystis sp. (35.3%), Aphanizomenon flos-aquae (30.2%), Aphanocapsa sp. (10.4%)

2002 45.4 Chroococcus dispersus (44.0%), Phormidium mucicola (19.3%), Aphanizomenon flos-aquae (11.8%), Anabaena spiroides

(11.7%)

2003 52.0 Microcystis sp. (34.2%), Phormidium mucicola (25.2%), Chroococcus dispersus (18.7%), Anabaena flos-aquae (17.8%)

2004 46.6 Microcystis sp. (27.6%), Anabaena flos-aquae (20.1%), Microcystis viridus (17.4%), Phormidium mucicola (12.1%)

2005 41.1 Microcystis aeruginosa (21.7%), Phormidium mucicola (19.5%), Aphanizomenon flos-aquae (14.8%), Anabaena flos-aquae (12.0%), Anabaena spiroides

(10.6%),

2006 61.3 Anabaena spiroides (32.0%), Microcystis sp. (31.1%), Aphanizomenon flos-aquae (22.7%), Phormidium mucicola (11.6%)

2007 24.1 Aphanothece spp. (31.8%), Anabaena flos-aquae (24.2%), Anabaena sp. (20.2%), Microcystis sp. (17.1%),

2008 27.0 Microcystis aeruginosa (47.7%), Anabaena flos-aquae (20.0%), Aphanothece spp. (13.1%)

2009 38.0 Microcystis aeruginosa (33.3%), Anabaena flos-aquae (23.7%), Aphanizomenon flos-aquae (18.0%), Pseudanabaena limnetica (11.1%)



Table H3 Average within-month similarities and the major taxa contributing to this within-month similarity by more than 10% for each month at Missisquoi Bay, using the complete cyanobacterial data set

Month Average within-month similarity (%)

Major contributors per month (with percent contribution in brackets)

June 18.8 Microcystis sp. (54.4%), Aphanocapsa elachista (16.7%)

Chroococcus sp. (10.5)

July 25.7 Microcystis sp. (23.1%), Phormidium mucicola (18.2%), Chroococcus dispersus (13.9%), Anabaena flos-aquae (10.6%), Aphanizomenon flos-aquae (10.4%)

August 36.2 Anabaena flos-aquae (21.5%), Aphanizomenon flos-aquae (16.9%), Chroococcus dispersus (16.1%), Microcystis sp. (14.4%), Phormidium mucicola (10.9%), Anabaena spiroides (10.7)

September 37.0 Anabaena flos-aquae (31.1%), Aphanizomenon flos-aquae (27.6%), Microcystis sp. (20.2%),

October 17.2 Aphanothece spp. (31.5%), Microcystis sp. (18.2%), Aphanocapsa elachista (16.4%), Anabaena flos-aquae (12.2%)

Transform: Log(X+1)


SiteSite a

Site b

Site d2

Site d8

2D Stress: 0.22

Transform: Log(X+1)


Year2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2D Stress: 0.22

Transform: Log(X+1)


MonthMay

July

August

September

June

November

October

2D Stress: 0.22

Transform: Log(X+1)


LocationWest

East

2D Stress: 0.22

Figure H2 nMDS output for the reduced cyanobacterial data set, plotted for sites, years, months and location (east/west)



-6 -4 -2 0 2 4 6

PC1

-6

-4

-2

0

2

4

PC

2

SiteSite a

Site b

Site d2

Site d8

TP

TN

TN:TP (concts)

pH

TurbTEMP SURF

Secchi

-6 -4 -2 0 2 4 6

PC1

-6

-4

-2

0

2

4

PC

2

Year2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

TP

TN

TN:TP (concts)

pH

TurbTEMP SURF

Secchi

-6 -4 -2 0 2 4 6

PC1

-6

-4

-2

0

2

4

PC

2

MonthMay

July

August

September

June

November

October

TP

TN

TN:TP (concts)

pH

TurbTEMP SURF

Secchi

-6 -4 -2 0 2 4 6

PC1

-6

-4

-2

0

2

4

PC

2

LocationWest

East

TP

TN

TN:TP (concts)

pH

TurbTEMP SURF

Secchi

Figure H3 PCA output for the environmental data set, plotted for sites, years, months and location (east/west)

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