Prediction of Nuisance Blue-green Algal Growth in
North Carolina Waters
Val H. Smith
Department of Biology, OlOA
University of North Carolina
Chapel Hill, NC 27514
The work on which this publication is based was supported by
funds provided by the Water Resources Research Institute of The
University of North Carolina.
WRRI project Number 70076
May 1987
Acknowledgments
p his
research was funded by a grant from the Water Resources
Research ~nstituteof The University of North Carolina.
I am
very grateful to Dr. D.H. Moreau for making this grant available,
and Ms. L. Lambert and Ms. D. Ruffner for their patient assistance.
Data used in this study were very generously provided by
Dr. 3. Overton and Ms. D. Moody of the North Carolina Division of
Environmental Management, Raleigh, and by Dr. M. Rodriguez and
Dr. J.C. Knight of the Duke Power Company.
Access to additional
data which were not used in this study was also kindly offered by
Dr. B. Ward of the Carolina Power and Light Company, and by Dr.
C. Weiss and Dr. D. Francisco of the Department of Environmental
Sciences and Engineering of the University of North Carolina at
Chapel Hill.
I thank Dr. D. Vogt for helpful comments, and W.S.
Baillargeon for his assistance during this study.
The contents of this publication do not necessarily reflect
the views and policies of the Water Resources Research Institute,
nor does mention of trade names or commercial products constitute
their endorsement or recommendation for use by the Institute or
the State of North Carolina.
.
Abstract
In the state of North ~arolina,nuisance blooms of bluegreen algae have caused water quality problems in the Chowan and
Neuse Rivers, and the potential for similar blue-green problems
is still an unresolved question in new impoundments such as the
B.E. Jordan and the Falls of the Neuse reservoirs.
Few general
models have yet been developed for the prediction and the management of blue-green algal blooms in freshwaters. However, Smith
(1985) has recently developed a set of empirical models predicting the summer mean biomass of blue-green algae (mm3 m-3)
, and
models predicting the relative biomass of blue-greens in the
phytoplankton (Smith 1986).
These models can potentially be used
as tools to help manage North Carolina water resources experiencing nuisance blue-green algal growth.
However, these empirical
models were developed entirely from north temperate data, and
their applicability to North Carolina reservoirs is not known.
The objective of this research was to test the applicability of
these models to North Carolina reservoirs.
Statistical analysis of data from 34 reservoirs, collected
and made available by the State of North carolina Division of Environmental Management and by the Duke Power Company, suggested
that none of the above models for blue-green algae were applicable to North Carolina reservoirs.
The reasons for the lack
of fit are not yet clear, but may include effects of non-algal
turbidity, hydraulic flushing, and inorganic carbon availability.
iii
0
Table of C o n t e n t s
Paqe
ii
AC~OWLEDGMENTS........................................
iii
ABSTRACT...............................................
LIST OF FIGURES........................................
v
LIST OF TABLES.........................................
vi
CONCLUSIONS AND RECOMMENDATIONS.......................
. vii
................................
RESULTS ................................................
MATERIALS AND METHODS..
3
4
HYDRAULIC FLUSHING RATE...........................
22
INORGANIC CARBON AVAILABILITY.....................
23
INORGANIC TURBIDITY...............................
23
LITERATURE CITED.......................................
27
.
List of Figures
Paqe
Plot of summer mean total algal biomass versus
the summer mean concentration of total phosphorus
in north temperate lakes (closed triangles) and in
North Carolina .reservoirs (open squares)
............
6
Plot of the summer mean biomass of blue-green
algae versus the summer mean concentration of total
phosphorus in north temperate lakes (closed triangles)
8
and in North Carolina reservoirs (open squares)
.....
Plot of the predictions of Eq. 1 versus observed
blue-green algal biomass in North Carolina
reservoirs
..........................................
11
5
Plot of the predictions of Eq. 2 versus observed
blue-green algal biomass in North Carolina
reservoirs
12
Plot of the predictions of Eq. 3 versus observed
blue-green algal biomass in North Carolina
reservoirs
13
Plot of the predictions of Eq. 4 versus observed
blue-green algal biomass in North Carolina
resenroirs
14
Plot of the predictions of Eq. 5 versus observed
relative blue-green algal biomass in North Carolina
reservoirs
15
..........................................
..........................................
..........................................
.........................................
Plot of peak observed blue-green algal biomass versus
summer mean total phosphorus concentrations in North
Carolina reservoirs
16
................................
Plot of individual measurements of Secchi disk
transparency versus chlorophyll concentrations in
North Carolina reservoirs
...........................
18
List of Tables
Paqe
Results of a regression analysis to test for
differences in the response of total algal biomass
(TOTALVOL) in north temperate lakes and in North
Carolina reservoirs to total phosphorus (TP),
using a dummy variable (DUMMY)
.....................
Results of a regression analysis to test for
differences in the response of blue-green algal
biomass (BGVOL) in north temperate lakes and in North
~arolinaReservoirs to total phosphorus (TP),
using a dummy variable (DUMMY)
.....................
Results of a stepwise regression analysis of
the summer mean blue-green algal biomass in North
Carolina reservoirs (NCBG) on station depth (STADEPTH),
water column mixed depth (MIXDEPTH), and estimated
21
non-algal turbidity (NONALG)
.......................
Conclusions and Recommendations
Determinants of blue-qreen alqal qrowth
voirs.
As has been found for chlorophyll
North ~arolinareser-
a in lakes monitored
during the National ~utrophicationSurvey (Canfield and Bachmann
1981) and in midwestern reservoirs (Hoyer and Jones 1983), the
results of this study suggest that total phosphorus is most
likely the primary limiting factor for total algal biomass in
turbid North Carolina reservoirs.
However, the same is not true
for the summer mean biomass of blue-qreen alqae.
Tests of 4 published empirical models for blue-green algal
biomass (Smith 1985) and a model of blue-green algal relative
biomass (Smith 1986) clearly indicate that the response of bluegreen algal growth in North Carolina reservoirs to nutrients and
light availability differs from that observed in clear north temperate lakes.
Furthermore, stepwise regression analysis suggests
that blue-green algal biomass in North Carolina reservoirs is
much more strongly influenced by non-nutrient factors such as
station depth, water column mixed depth, and non-algal turbidity
(Eq. 1 ) .
Further analyses of data from North Carolina reser-
voirs and experimental studies are necessary to confirm these
conclusions, and to explore the factors which govern summer bluegreen algal growth in these systems.
It is suggested that these
studies include detailed experimental examinations of the roles
of inorganic suspended solids on nutrient availability, light extinction, and algal sedimentation.
vii
It is also suggested that the
roles of hydraulic flushing and inorganic carbon availability be
explored in future models of blue-green algal biomass in North
Carolina reservoirs.
Data limitations for future modellinq.
When this research was
proposed, it was expected that a large data set already existed
on phytoplankton and water chemistry in North Carolina waters.
Contacts were then made with a number of individuals to assess
the availability and suitability of these data.
For example, an
-
extensive data set was offered by Dr. Charles Weiss and coworkers in the Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill.
Similarly, a
large data base was offered by Dr. Bobby Ward of the North
Carolina Power and Light Company.
However, on close inspection
these data proved not to be applicable to this study because the
phytoplankton measurements were not consistent with the microscopical methods used in the temperate lakes studies from which
the models were originally developed.
Furthermore, total
nitrogen is an important chemical variable which is apparently
not consistently measured by all investigators.
Finally, given
the potential importance of non-algal turbidity in these systems,
it is suggested that the concentration of inorganic suspended
solids be directly measured in future investigations.
In order to assure that the data collected can be universally used and interpreted, it is strongly recommended that those
involved in the monitoring of water quality in the State of North
viii
Carolina use standard methods of biological and chemical analysis
in the future.
It is also vital that future water quality
modelling efforts be based on data generated using consistent
chemical and microscopical methods.
Introduction
Recent empirical phosphorus loading models and phosphoruschlorophyll models (Vollenweider 1968, 1976; Dillon and Rigler
1974, 1975; Jones and Bachmann 1976; Hoyer and Jones 1983; OECD
1982: Smith 1982; Walker 1984) provide a rational management
basis for the management of water quality based on nutrients,
chlorophyll g , and transparency.
However, in many situations
other water quality criteria may be of greater importance in
making management decisions.
For example, in many lakes and
reservoirs the absence of hypolimnetic oxygen is an undesirable
condition, and numerous empirical models predicting hypolimnetic
oxygen depletion rates have been developed and are now in use
(e-g. OECD 1982; Walker 1984).
Furthermore, a recent model pre-
dicting the occurrence of undesirable summer fishkills from total
phosphorus concentrations in the lake water has been developed by
Mericas and Malone (1984).
Similarly, the concentration of chlorophyll 2 alone is not a
good predictor of the reduced water quality associated with bluegreen algal dominance in the phytoplankton.
Research in North
Carolina has identified blue-green algal blooms as a major
problem in the Lower Chowan River (Witherspoon et al. 1979; Paerl
1982), in the Neuse River (Christian et ale 1986: Paerl 1983,
1987), and in thermally-enriched, offstream systems such as Sutton Lake near Wilmington (Re Yates, Carolina Power and Light,
pers. comm.).
Blue-green algae thus present a significant water
*'
quality problem in a number of North ~arolinawaters, and
Witherspoon et ale (1979) have indicated that there is a need for
models of blue-green algal growth which will allow us to predict
the occurrence of blue-green blooms, and which will allow us to
make rational cost-benefit analyses of potential nutrient control
measures.
Smith (1985) has recently developed a series of empirical
models predicting the summer mean biomass of blue-green algae
(BG, g m-3) in north temperate lakes from total phosphorus (TP,
mg mW3) , total nitrogen (TN, mg m-3) , and lake mean depth (2, m) :
(1)
log BG = -1.371
+ 0.979 log TP, r2
(2)
log BG = -0.142
+ 0.596 log TP - 0.963 log Z, r2
(3)
log BG = -2.994 + 1.244 log TN
(4)
log BG
=
2.203
-
-
0.835 log TN:TP
=
0.71.
= 0.80.
0.751 log Z, r2 = 0.79.
-
1.336 log Z, r2
=
0.77.
In addition, Smith (1986) has developed an empirical model predicting the summer relative biomass of blue-green algae (%BG, the
proportion of the total algal biomass which is represented by
blue-greens).
In Eq. 5, SD is the summer mean Secchi disk
transparency (in meters), and Zm is the summer mean mixed depth
for the water column (meters):
(5)
logit (%BG) = 2.358
-
-
1.297 log TN
2.058 log SD
+
+
0.692 log TP
0.538 log Z
,
,
r2 = 0.52.
These models potentially may provide tools for the prediction of
blue-green algal growth in North Carolina waters.
However, Eq.
1-5 were developed from a data set containing only north temperate lakes, and the applicability of these models to southeastern waters has not been evaluated.
Recent research (Higgins and
Kim 1981; Walker 1982; Carriker and Taylor 1984; Pearse 1984;
Reckhow and Clements 1984) has in fact suggested that models
developed from north temperate lakes may not apply to the warmer,
more turbid water resources of the southeastern United States.
The purpose of this research was thus to evaluate the applicability of Eq. 1-5 to North Carolina reservoirs.
Materials and Methods
The data needed to test these models were generously
provided for sampling stations in 32 North Carolina reservoirs by
Dr. J. Overton and Ms. D. Moody of the North Carolina Division of
Environmental Management, Raleigh, and for 2 reservoirs by Dr. M.
Rodriguez and Dr. J.C. Knight of the Duke Power Company.
These
data included individual measurements of water chemistry, Secchi
disk transparency or vertical light extinction coefficients, sampling station total depth and mixed depth, chlorophyll
a,
and al-
gal biomass during the months of May-September for each year of
record.
For algal cell volume, it was assumed that lo3 mm3 mo3
was equivalent to 1 g m-3 wet weight (Smith 1985).
These data
were compiled and subjected to statistical analysis using the
SYSTAT software (Wilkinson 1986) on an IBM PC-AT microcomputer.
.
The overall data set, containing the measurements from all
sampling dates, was used to examine water quality relationships
for individual sampling dates.
However, summer means of each
variable were needed to test Eq. 1-5, and sufficient data to calculate May-September means were only available for stations in
the B.E. Jordan ~eservoir,the Falls of the Neuse Reservoir, Lake
Norman, and Lake Wylie.
The analyses discussed below thus are
based primarily on a reduced data set containing the calculated
summer means from stations in these
4
reservoirs. I n t h e reduced
iata'set, each case is represented by one summer mean for each
station sampled, for each year of record.
Copies of the data are
available on floppy disk from the author.
Results
Prior to statistical analysis, the data from north temperate
lakes (Smith 1985) were pooled with summer mean data from sta-
A
tions in North Carolina reservoirs from the reduced data set.
dummy variable was used to code the data by location (DUMMY=O if
lake=temperate, and DUMMY=l if lake=NC), and differences in the
response of algae to total phosphorus in the two locations were
then explored using 'this dummy variable.
In this analysis, the
presence of a statistically significant (P<0.05) coefficient for
the dummy variable indicates a significant difference between the
response of algae to total phosphorus in the two lake types (cf.
Kleinbaum and Kupper 1978).
The dummy variable analysis indicated no significant difference in the response of total algal biomass to phosphorus in
clear lakes and North Carolina reservoirs (Table 1).
As can be
seen in Fig. 1, the summer mean values for total algal biomass
from stations in 4 North Carolina reservoirs (open squares) were
consistent with data from the north temperate lakes (closed
triangles), with the exception of one outlier from the Falls of
the Neuse Reservoir (station 13).
r
A
similar pattern was not evident, however, when the data
for blue-green algal biomass were plotted for each of these same
stations.
As can be seen in Fig. 2, three clusters of outliers
are evident:
four points from Lake Norman (station ll), two
points from Lake Wylie (station 244), and three points from the
Falls of the Neuse Reservoir (station 13).
A dummy variable
analysis of the entire data set confirmed a highly significant
(P = 0.002) difference in the response of blue-green algae to
Table 1.
Results of a regression analysis to test for differences
in the response of total algal biomass (TOTALVOL) in north temperate
lakes and in North Carolina reservoirs to total phosphorus (TP),
using a dummy variable (DUMMY).
DEPENDENT VAR1ABLE:LOG TOTALVOL
ADJUSTED SQUARED MULTIPLE R
VARIABLE
COEFFICIENT
=
DF= 74
0.637
SQUARED MULTIPLE R
STD ERROR
P(2 TAIL)
LOGTP
DUMMY
ANALYSIS OF VARIANCE
SUM-OF-SQUARES
REGRESSION
RESIDUAL
DF
0.647
STANDARD ERROR OF ESTIMATE:0.232
CONSTANT
SOURCE
=
MEAN-SQUARE
F-RATIO
m
100,000
,
SUMMER MEAN TOTAL ALGAL BIOMASS
IN N . TEMPERATE LAKES AND NC RESERVOIRS
A LAKES
RESERVOIRS
F i g . 1. P l o t o f s u m m e r m e a n t o t a l a l g a l b i o m a s s v e r s u s t h e
summer mean c o n c e n t r a t i o n o f t o t a l p h o s p h o r u s i n n o r t h
temperate l a k e s (closed t r i a n g l e s ) and i n 3 o r t h Carolina
r e s e r v o i r s (open s q u a r e s ) .
Table 2. Results of a regression analysis t o test for differences in
the response of blue-green algal biomass (BGVOL) in north temperate
lakes and in North Carolina Reservoirs t o total phosphorus (TP),
using a dummy variable (DUMMY).
DEPENDENT VAR1ABLE:LOG BGVOL
DF=74
ADJUSTED SQUARED MULTIPLE R = 0.453
VARIABLE
COEFFICIENT
SQUARED MULTIPLE R = 0.468
STANDARD ERROR OF ESTIMATE:0.538
STD ERROR
CONSTANT
LOG TP
DUMMY
ANALYSIS OF VARIANCE
SOURCE
SUM-OF-SQUARES
REGRESSION
RESIDUAL
DF
MEAN-SQUARE
F-RATIO
SUMMER MEAN BLUE-GREEN ALGAL BIOMASS
3
100,000
IN N . TEMPERATE LAKES AND NC RESERVOIRS
LAKES
RESERVOIRS
Fig. 2.
P l o t o f t h e summer m e a n b i o m a s s o f b l u e - g r e e n a l g a e
v e r s u s t h e summer m e a n c o n c e n t r a t i o n o f t o t a l p h o s p h o r u s i n
n o r t h temperate l a k e s (closed t r i a n g l e s ) and i n North
C a r o l i n a r e s e r v o i r s (open s q u a r e s ) .
phosphorus in north temperate lakes and North Carolina reservoirs
(Table 2) .
Because the mean depths of the individual reservoir segments
sampled by the North Carolina Division of Environmental Management and by Duke Power were not immediately available, station
depth was substituted for segment mean depth in further analyses
of the data.
These analyses indicated that Eq. 1-4 were
uniformly poor predictors of blue-green algal biomass in North
Carolina reservoirs (Fig. 3-6), and that Eq. 5 could not be used
to predict the relative biomass of blue-green algae in these
.
reservoirs (Fig. 7)
Although the summer mean biomass of blue-green algae can be
used as an indicator of water quality, the summer peak biomass of
blue-greens may be of more interest since it represents the worst
case and may be of much greater concern to both industrial and
recreational users of lakes.
Data from the Jordan and Falls
reservoirs and from Lake Norman and Lake Wylie were thus used to
generate an additional data set containing the summer peak observed blue-green algal biomass.
However, no consistent
relationship could be found between peak blue-green biomass and
total phosphorus in these reservoirs (Fig. 8).
In an attempt to determine which factors were responsible
for the scatter observed in Fig. 2-7, further analyses were made
of the overall data set.
In order to evaluate the possible role
10,000
,
PREDICTED BLUE-GREEN ALGAL BIOMASS
MODEL 1
100
1000
10,000
3
-3
LOG OBSERVED BLUE-GREEN BIOMASS, mm rn
F i g . 3.
P l o t of t h e p r e d i c t i o n s of E q . 1 v e r s u s observed
blue-green a l g a l biomass i n North Carolina r e s e r v o i r s .
100,000
,
PREDICTED BLUE-GREEN ALGAL BIOMASS
MODEL 4
3 -3
LOG OBSERVED BLUE-GREEN BIOMASS, mm m
Fig. 6.
P l o t of the predictions of Eq. 4 versus observed
blue-green a l g a l biomass i n North Carolina r e s e r v o i r s .
b 'TI
r
PREDICTED LOGIT(percent blue-green algae)
P-
C 00
fD
I
00 w
Y
o
fD
fD
s
'd
r
D O
Prt
00
D 0
rn,
~
r
fD
3
r
fD
a,
rt 'd
P-'.Y
C
f~
fD
a
P
d O
I-'. rt
0 .'-t
0
Pis
m
m
V,
0
P n,
3
M
za
0
Y
rt Ln
3
4
0 fD
a, Y
Y m
0 C
r
m
t-'.
3 0
b
P,
m
Y f D
fD Y
m C
fD
Y
C
0
P
Y
m
fD
a
t
m
' - 100,000
SUMMER PEAK BLUE-GREEN ALGAL BIOMASS
IN NORTH CAROLINA RESERVOIRS
100
LOG TOTAL PHOSPHORUS, mg
m3
Fig. 8.
P l o t of peak o b s e r v e d b l u e - g r e e n a l g a l biomass v e r s u s
summer mean t o t a l p h o s p h o r u s c o n c e n t r a t i o n s i n N o r t h C a r o l i n a
reservoirs.
of inorganic turbidity, an estimate of non-algal turbidity was
calculated for each station from measurements of Secchi disk
transparency.
In the case of Lake Wylie and Lake Norman, ~ e c c h i
disk transparency (SD, meters) was first calculated from measurements of vertical light attenuation (k, meters-')
using the
relationship of Megard (1980):
Non-algal turbidity (NON-ALGAL, meters-')
was then calculated em-
pirically for each sampling date in the overall data set using
the method of Walker (1982).
A plot was first made of Secchi
disk transparency versus measured concentrations of chlorophyll g
(CHL, mg m-3
;
cf. Fig. 9), and then values of A and B in the
generalized relationship
were selected to provide good empirical fits to the upper and
lower boundaries of the data.
In Eq. 7, the value of A is an es-
timate of non-algal turbidity, and B is an estimate of the ex-
SUMMER SECCHI DISK TRANSPARENCY
IN NORTH CAROLINA RESERVOIRS
CA
g
I-
10
2
-
0
-
>-I
7
W
CK
4
a
CA
7
4
LT
t1L
CA
ii
1/SD = 0.15 + 0.003CHL
- i
-
I - .
-
..
.
.
-
.
-
I
0.1-
8
W
c/)
(3
0
J
.. . .
.. .
I
I.
Dm..
I
.
I/SD=20+0.003CHL
-
rn
-
I
I
#
I
I
I
I
I
I
L
I
1
-
I
I
1
10
LOG CHLOROPHYLL a, rng
1
I
I
I
100
m3
F i g . 9. P l o t o f i n d i v i d u a l m e a s u r e m e n t s o f S e c c h i d i s k
transparency versus chlorophyll concentrations in North
Carolina reservoirs.
tinction due to chlorophyll q.
This method provided an upper
boundary of
and a lower boundary of
1/SD = 20
+
0,003 CHL.
The value for B = 0.003 rn2 (mg C H L )
obtained from this analysis
(Eq. 8-9) is lower than the typical values cited by Walker
(1982), but nonetheless provides a good fit to the observed data.
Using this value of B, it was then possible to estimate the magnitude of non-algal turbidity from measurements of Secchi disk
transparency and chlorophyll on each sampling date as
NON-ALGAL = 1/SD
-
0.003 CHL.
Summer mean values of non-algal turbidity were then calculated
for each sampling station represented in the reduced data set,
and regression analyses were then run to determine the best model
for summer mean blue-green algal biomass (NCBG, mm3
Carolina reservoirs.
in North
The stepwise regression analysis included
total nitrogen, total phosphorus, station depth, mixed depth, and
non-algal turbidity as candidate variables.
surprisingly,
however, this analysis suggested a predictive model which included only morphometry and turbidity (Table 3 ) :
(11)
log NCBG = 4.129
-
+ 3.506 log MIXED DEPTH
4.094 log STATION DEPTH
-
0.165 log NONALGAL TURBIDITY, r2=0.52.
Simple regression analysis further indicated that the summer mean
blue-green biomass in these reservoirs was only very weakly
correlated with total phosphorus:
Discussion
Based on the regression analyses discussed above, the
response of summer mean total biomass of algae to total phosphorus appears to be similar in clear lakes and in North ~arolina
Table 3.
Results of a stepwise regression analysis of the summer
mean blue-green algal biomass in North ~arolinareservoirs (NCBG) on
station depth (STADEPTH), water column mixed depth (MIXDEPTH), and
estimated non-algal turbidity (NONALG).
DEPENDENT VARIABLE: LOG NCBG
ADJUSTED SQUARED MULTIPLE R
DF= 45
=
0.486
SQUARED MULTIPLE R
0.521
STANDARD ERROR OF ESTIMATE:0.459
VARIABLE
COEFFICIENT
STD ERROR
CONSTANT
4.129
0.333
12.412
<0.001
LOG STADEPTH
-4.094
0.633
-6.468
<0.001
LOG MIXDEPTH
3.506
0.707
4.960
<0.001
-0 .165
0.066
-2.487
0.017
NONALG
=
P(2 TAIL)
ANALYSIS OF VARIANCE
SOURCE
SUM-OF-SQUARES
DF
MEAN-SQUARE
F-RATIO
REGRESSION
9 397
3
3.132
14,869
RESIDUAL
8.637
41
0.211
.
<O 001
reservoirs (Fig. 1, Table 1).
However, response of both the ab-
solute and the relative biomass of blue-sreen alsae to total
phosphorus appears to differ significantly in the two types of
systems.
The reasons for these differences are not yet clear,
but may include factors such as hydraulic flushing rate, inorganic carbon availability, and inorganic turbidity.
Hvdraulic flushinq rate.
An additional factor which may have in-
fluenced the response of blue-green algae to nutrients is the
hydraulic flushing rate.
The flushing rate of most artificial
impoundments is significantly higher than that of natural lakes
(Walker 1984).
If the hydraulic flushing rate is sufficiently
rapid, many algal species will not have sufficient time to grow
during their brief residence time in the reservoir.
Weiss et al.
(1981) and Uttormark and Hutchins (1985) have in fact suggested
that slow-growing blue-green algae may be held in check by the
rapid flushing rates observed in lakes and reservoirs with extensive fluvial inputs.
This mechanism would also act to minimize
the likelihood of blue-green algal blooms even under conditions
of high nutrient loading to the reservoir.
Unfortunately, this hypothesis could not be directly tested
in this study.
Although some estimates of water flows between
segments of the B. Everett Jordan reservoir have been made by
Moreau and Challa (1985), estimates of the hydraulic flushing
rates for all the individual reservoir segments analyzed here
were not available.
If possible, hydraulic flushing rate should
be included as candidate variable in the development of future
models for blue-green algal growth in North ~arolinareservoirs.
Carbon dioxide.
The environmental factors which have been ex-
perimentally demonstrated to favor dominance by blue-green algae
also include low inorganic carbon availability (King 1970; Talling 1976; Shindler et al. 1980; Shapiro 1973, 1984; Paerl and Ustach 1982; Paerl 1987).
However, only one large-scale study has
examined the effects of inorganic carbon on the growth of bluegreen algae in a broad cross-section of lakes.
Smith et al.
(1987) have concluded that total C02 was indeed an important
determinant of the peak summer biomass of the bloom-forming bluegreen Aphanizomenon flos-aquae in Swedish lakes, and experimental
studies suggest that it may be important for other species as
well (cf. Shapiro 1983).
Inorganic carbon availability can be
easily calculated from pH, temperature and alkalinity using
tables in Mackereth et al. (1978), and should also be considered
in future modelling efforts.
Inorsanic turbiditv.
Perhaps the most likely factor influencing
the growth of blue-green algae in North Carolina reservoirs is
the presence of high concentrations of inorganic suspended
solids.
Research on midwestern and southeastern U.S. reservoirs
has shown that high levels of inorganic (non-algal) turbidity
have a marked effect on the productivity of each trophic level
(Marzolf 1984).
For example, the presence of high concentrations
of inorganic suspended solids clearly can reduce the concentra-
tion of chlorophyll a produced at a given concentration of total
phosphorus (Hoyer and Jones 1983; Pearse 1984), and it is important to note that the outlier in the relationship between total
phosphorus and total algal biomass in ~ i g .1 is the most turbid
station in the reduced data set.
Nonetheless, Fig. 1 suggests
that the total algal biomass observed in these 4 North Carolina
reservoirs was typically consistent with that expected from
epilimnetic concentrations of total phosphorus.
The same was not true of blue-green algal biomass, however
(Fig. 2).
It is possible that this difference in the behavior of
total algal biomass and blue-green algal biomass results from a
differential effect of inorganic suspended solids on the growth
of the different algal taxa.
In a study of four eutrophic
Nebraska impoundments, Hergenrader (1980) found that blue-green
algae were much less important in the most turbid system, suggesting that the presence of high levels of inorganic turbidity
acted to reduce the likelihood of blue-green blooms in eutrophic
reservoirs.
Weiss et al. (1981) similarly concluded that high
turbidities and rapid hydraulic flushing limited the possibility
of blue-green blooms in eutrophic High Rock Lake, North ~arolina.
At least two potential mechanisms could act to reduce the
ecological success of blue-green algae in reservoirs having high
concentrations of suspended silts and clays.
First, the pub-
lished literature has documented the fact that dissolved inorganic phosphorus interacts strongly with suspended soil particles
(e.g. Green et al. 1978; Logan et al. 1979; Beek and van
Reimsdijk 1982; Kuenzler and Greer 1980; Kuenzler et al. 1986).
Weiss et al. (1981) noted that phosphorus, the principal limiting
nutrient in High Rock Lake, appeared to be readily removed from
the water column by binding to clay particles and was subsequently lost via sedimentation or discharge from the reservoir.
Cuker (1985) has in fact found that suspended clays and phosphorus interact to regulate algal primary productivity in a small
Piedmont North Carolina lake.
Fertilization of a series of ex-
perimental enclosures with phosphorus alone stimulated the growth
of the blue-green alga Anabaena, but the subsequent addition of
kaolinite clay reduced the growth of Anabaena.
This reduction in
growth could be mitigated by the addition of further inorganic
phosphorus.
The second major mechanism involves the enhancement of
phytoplankton loss rates from the water column by sedimentation.
Avnimelich et al. (1984) have found that the presence of
suspended silts and clays causes the flocculation and sedimentation of algae.
Furthermore, this interaction was dependent on
the species of algae present, and may preferentially remove bluegreens from the water column relative to other species.
Another important effect of inorganic suspended solids is
their influence on light penetration and scattering.
The
presence of high concentrations of suspended soil particles
results in a reduction in the depth of the euphotic zone, and a
reduction of light availability in the mixed layer.
However,
blue-green algae are thought to be competitively superior under
conditions of low light availability (cf. Smith 1986).
Examples
of turbid systems exhibiting nuisance blue-green algal blooms are
the Kentucky Reservoir, USA (Carriker and Taylor 1984) and Lake
Burley Griffin, Australia (Rosich and Cullen 1981).
It is clear from the above discussion that the effects of
silt and clay turbidity on the intensity and the frequency of
blue-green algal blooms are not yet well understood, and should
be examined in much greater detail.
It is suggested that further
experimental studies of the effects of inorganic turbidity
similar to those performed by Cuker (1985) be made to quantify
the effects of suspended silt and clay on nutrient availability,
light availability, blue-green algal growth, and blue-green algal
sedimentation.
Furthermore, future modelling efforts should con-
tinue to include inorganic turbidity as a candidate.variable.
However, non-algal turbidity should be measured directly as concentrations of inorganic suspended solids (Hoyer and Jones 1983),
rather than estimated indirectly as in this study.
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