The Science of the Total Environment 288 Ž2002. 167᎐181
Biological and chemical factors influencing shallow lake
eutrophication: a long-term study
S.S.S. Lau a,U , S.N. Lane b
a
Department of Geography, Uni¨ ersity of Cambridge, Downing Place, Cambridge CB2 3EN, UK
b
School of Geography, Uni¨ ersity of Leeds, Leeds LS2 9JT, UK
Received 9 April 2001; accepted 20 June 2001
Abstract
The focus of eutrophication research has tended to be upon short-term and experimental studies. However, given
the range of factors that can influence eutrophication dynamics, and that these matter over a range of time scales,
some discrete, some continuous, eutrophication dynamics may only be fully investigated when long-term, time-series
data are available. The present study aims to evaluate the interacting effects of abiotic processes and biotic dynamics
in explaining variations of phytoplankton biomass in a eutrophic shallow lake, Barton Broad ŽNorfolk, UK. using a
long-term data set. Multivariate statistical analysis shows that the inter-relationships between phytoplankton
variability, nutrient and grazing factors were highly sensitive to seasonal periodicity. In spring phytoplankton biomass
was related to phosphorus, nitrogen and silicon. In summer phytoplankton biomass was associated with phosphorus,
nitrogen and zooplankton. In autumn phytoplankton was related to phosphorus, nitrogen, silicon and zooplankton. In
winter, no significant relationship could be established between phytoplankton and environmental variables. This
paper improves our understanding of the governing role of nitrogen, phosphorus, silicon and zooplankton upon
phytoplankton variability, and hence, improves management methods for eutrophic lakes. 䊚 2002 Elsevier Science
B.V. All rights reserved.
Keywords: Daphnia; Norfolk Broads; Phytoplankton; Temporal variability
1. Introduction
The focus of eutrophication research has
tended to be upon the pattern and cause of
variability in eutrophication and the subsequent
biological response Že.g. Weyhenmeyer et al.,
1999.. It has made particular reference to changes
U
Corresponding author. Tel.: q44-1223-333399; fax: q441223-333392.
E-mail address: [email protected] ŽS.S.S. Lau..
in phytoplankton biomass and tended to use
short-term and experimental studies Že.g. PintoCoelho, 1998.. However, given the range of factors that can influence eutrophication dynamics,
and that these matter over a range of time scales,
some discrete, some continuous, eutrophication
dynamics may only be fully investigated when
long-term, time-series data are available ŽDegobbis et al., 2000.. In particular, long-term investigations may be crucial to tackling the problems
arising from the vast number of interacting vari-
0048-9697r02r$ - see front matter 䊚 2002 Elsevier Science B.V. All rights reserved.
PII: S 0 0 4 8 - 9 6 9 7 Ž 0 1 . 0 0 9 5 7 - 3
168
S.S.S. Lau, S.N. Lane r The Science of the Total En¨ ironment 288 (2002) 167᎐181
ables involved in eutrophication processes and
the relative singularity of particular eutrophic
events. In particular, long-term investigation may
provide a realistic means of establishing variable
interactions in ecosystem studies ŽStraile and
Adrian, 2000., leading to identification of certain
common characteristics, in particular predator᎐prey interactions and nutrient cycling in relation to lake ecosystem behaviour ŽAdrian et al.,
1995; Adrian, 1997; Kozerski et al., 1999..
With this in mind, this paper uses data from
Barton Broad ŽNorfolk, UK., which has a relatively long-term monitoring record. The study attempts to investigate variability in abiotic and
biotic processes and their interactions in shallow
lake eutrophication. The analysis and evaluation
of the long-term data are permitted by more
powerful multivariate statistical modelling for
dealing with large, inter-relating data collected
over a large temporal scale, and by the growing
emphasis on nitrogen-limitation of phytoplankton
biomass ŽLau, 2000.. The aims of the study were:
Ža. to characterise phytoplankton dynamics with
an array of interrelated abiotic and biotic variables; Žb. to identify the controlling factorŽs. governing changes in phytoplankton biomass; and Žc.
to elucidate interactions between bottom-up
Žnutrient manipulation. and top-down Žgrazing
effects . upon the phytoplankton dynamics of the
lake ecosystem in the context of temporal variability. Specifically, the paper considers the relative role of nutrient Ži.e. nitrogen, phosphorus
and silicon. and grazing Ži.e. zooplankton and
Daphnia. factors and their interactions upon phytoplankton biomass with respect to seasonal periodicity.
2. Materials and methods
flora, supporting a diverse fauna of invertebrates
ŽMason, 1990.. The area as a whole at the time
was outstanding for its wildlife interest, but now
maintains a large tourist industry, based mainly
upon boating holidays ŽMason, 1996.. Since the
late 1950s, conditions in the Norfolk Broads have
deteriorated. Most of the Broads are no longer
clear and the majority of the Broads have undergone severe eutrophication with aquatic plants
increasingly replaced by unwanted concentrations
of phytoplankton ŽBoorman and Fuller, 1981..
This was associated with the increase of human
activities in the catchment area, leading to enhanced nutrient runoff due to agricultural intensification. Since 1977, restoration measures to deal
with eutrophication have been adopted, including
external nutrient reduction, suction dredging,
isolation, and biomanipulation. In the case of
Barton Broad, despite attempted restoration by
nutrient reduction, the water has remained turbid
and submerged macrophytes have not re-established themselves ŽPhillips and Chilvers, 1991..
The Broad, amongst others in the region, has
been researched intensively for some time. It has
a water surface area of 72 ha and a water volume
of 804 = 10 3 m3. It is a shallow Ž1᎐2-m water
depth., highly eutrophic lake that previously had
a major phosphorus supply via the sewage treatment works that discharges into the River Ant.
The bottom of the Broad consists of a fine, organic mud, overlying peat. Barton Broad itself lies
in the main valley of the River Ant, a shallow,
slow flowing, navigable river which forms the
principal inflow and outflow of the Broad ŽPhillips, 1984.. During the winter, the lake is effectively flushed, with a water residence time of
approximately ten days. During the summer, when
river discharge is reduced, water residence times
are thought to be 20᎐30 days ŽPhillips and Kerrison, 1991..
2.1. Pollution history of the study site
2.2. Sampling and data retrie¨ al
Barton Broad Ž52⬚44⬘N, 1⬚30⬘E; Fig. 1. is the
second largest broad in the Norfolk Broads, UK.
The Broad was formed in the 14th century AD
when peat workings in the area were flooded
ŽMoss, 1987.. At the end of the 19th century, the
lake contained an abundant and diverse aquatic
Data were retrieved and compiled from a
database held at the Haddiscoe Laboratory of the
Environment Agency near Great Yarmouth, Norfolk ŽUK.. Sampling was carried out both by
Anglian Water and the Environment Agency, at
S.S.S. Lau, S.N. Lane r The Science of the Total En¨ ironment 288 (2002) 167᎐181
169
Fig. 1. Map of Barton Broad ŽNorfolk, UK..
an interval of weekly to bi-weekly in March᎐
August and monthly in September᎐February for
the period 1983᎐1993. Surface water samples were
taken as representative of the entire water
column, which is well mixed as a result of the
shallow water depth. Five water samples taken
from across the lake at each sampling date, were
collected to form a composite sample for all analyses. Samples were filtered through Whatman
GFrC filters at the time of collection. Chlorophyll a was determined by extraction with cold
90% acetone ŽMoss, 1967.. Zooplankton were
sampled using a 10.3-cm diameter tube to collect
an integrated sample of a 1-m water column
170
S.S.S. Lau, S.N. Lane r The Science of the Total En¨ ironment 288 (2002) 167᎐181
Table 1
A summary of methodology ŽStrickland and Parsons, 1968; Mackereth et al., 1978.
Parameter
Symbol
Unit
y1
Total oxidised nitrogen
TON
mg l
Ammoniacal nitrogen
Total phosphorus
Soluble reactive phosphorus
Reactive dissolved silicon
NH4
TP
SRP
Si
mg ly1
mg ly1
mg ly1
mg ly1
below the water surface. Animals were removed
by passing the collected water through a 65-␮m
mesh size nylon plankton net and preserved in
) 70% ethanol. The samples were stored at 4⬚C
and were normally analysed within a day. Maximum storage time was less than 3 days after
sampling. Table 1 shows a summary of the analytical methodology for nutrients. The methodology
of sample collection and analysis are detailed in
Phillips and Kerrison Ž1991. and detection limits
in Holdway et al. Ž1978..
2.3. Data preparation
Average annual and seasonal data, based on
pooled samples, were determined ŽTables 2 and
3.. The phytoplankton biomass were represented
by the chlorophyll a concentration. Among the
zooplankton, Daphnia play a key role in the control of phytoplankton density ŽDeMott and Kerfoot, 1982; Vanni, 1986.. Thus, phytoplankton
grazers Žcounts. were represented by two groups:
total zooplankton ŽZoo.; and Daphnia ŽDap..
Nutrients were represented by three groups:
phosphorus wtotal phosphorus ŽTP., and soluble
reactive phosphorus ŽSRP.x; nitrogen wtotal oxidised nitrogen ŽTON., and ammoniacal nitrogen
ŽNH 4 .x; and silicon wreactive dissolved silicon ŽSi.x.
On some occasions, a value for a particular
parameter was not obtainable. The original incomplete data sets Ži.e. with missing data. were
used in the study, without interpolation of missing
values.
2.4. Statistical analysis
The structure of the environmental variability
in the data set was explored using a multivariate
Methodology
Sulphanilamide method for nitrate;
Brucine method for nitrite
Berthelot method
Acid digestion and molybdenum blue method
Molybdenum blue method
Reaction with acid sodium molybdate
statistical approach. Two parametric multivariate
analyses Ži.e. factor and regression analyses . were
employed to examine the possible influence of
various environmental variables on phytoplankton. These methods are widely used in ecological
studies and have proved to be useful for understanding interactions between the ecological factors that influence plankton communities in highly
complex systems ŽVan Tongeren et al., 1992;
Romo and Tongeren, 1995; Romo et al., 1996..
However, it has been argued that time-series data
collected through monitoring programs inherently
have characteristics Ži.e. autocorrelation between
two consecutive data points. that violate the basic
assumptions of parametric procedures ŽMomen et
al., 1996.. The problem is overcome by the fact
that life cycle of phytoplankton is a matter of a
few days Ži.e. less than a week; Reynolds, 1984.,
leading to minimal collinearity. Preliminary data
manipulation, including removal of seasonality
effects by separate analysis of spring, summer,
autumn and winter data, and standardisation Žby
subtracting the mean and dividing by the S.D..
ŽManly 1986., precluded the need for non-parametric procedures. Observations that are not
spaced equally over time precluded the use of
time-series analysis.
Factor analysis Žbased on principal components
analysis ., and simple and multiple linear regression analyses were undertaken. Factor analysis
was particularly useful for considering several related random environmental variables simultaneously, and so identifying a new, smaller set of
uncorrelated variables that accounted for a large
proportion of the total variance in the original
variables. In order to account for nutrient and
grazing factors in phytoplankton variability, seven
S.S.S. Lau, S.N. Lane r The Science of the Total En¨ ironment 288 (2002) 167᎐181
171
Table 2
Chlorophyll a Ž␮g ly1 . and nutrients Žmg ly1 . in Barton Broad in 1983᎐1993
Chl a
TON
NH4
TP
SRP
Si
1983᎐1993
a
130 " 92.1
Ž1.60᎐542.b
230c
1.15" 1.17
Ž0.10᎐10.2.
206
0.08" 0.12
Ž0.01᎐0.72.
221
0.150" 0.084
Ž0.028᎐0.472.
227
0.015" 0.025
Ž0.000᎐0.180.
225
9.56" 6.31
Ž0.02᎐25.0.
206
1983
91.1" 54.1
Ž3.70᎐234.
24
1.19" 0.98
Ž0.10᎐2.60.
12
0.10" 0.11
Ž0.01᎐0.50.
25
0.130" 0.043
Ž0.075᎐0.211.
24
0.015 " 0.034
Ž0.000᎐0.153.
23
7.55" 5.07
Ž0.21᎐15.8.
21
1984
127 " 50.9
Ž26.0᎐209.
19
1.03" 0.95
Ž0.10᎐3.50.
16
0.03" 0.03
Ž0.01᎐0.08.
17
0.164" 0.073
Ž0.038᎐0.314.
19
0.013" 0.018
Ž0.000᎐0.069.
19
5.14" 5.60
Ž0.02᎐16.5.
17
1985
97.1" 62.1
Ž11.0᎐215.
22
2.08" 2.36
Ž0.10᎐10.2.
19
0.13" 0.14
Ž0.01᎐0.46.
20
0.093" 0.054
Ž0.028᎐0.243.
20
0.038" 0.047
Ž0.000᎐0.180.
18
6.23" 4.82
Ž0.21᎐13.7.
20
1986
136 " 62.7
Ž39.0᎐258.
21
1.09" 1.13
Ž0.10᎐4.50.
20
0.06" 0.06
Ž0.01᎐0.26.
18
0.158" 0.058
Ž0.060᎐0.282.
20
0.009" 0.015
Ž0.001᎐0.060.
21
9.41" 6.58
Ž0.21᎐21.2.
14
1987
91.3" 57.7
Ž7.10᎐225.
19
1.38" 0.97
Ž0.10᎐3.40.
19
0.14" 0.19
Ž0.02᎐0.60.
19
0.116" 0.064
Ž0.028᎐0.259.
19
0.011" 0.018
Ž0.001᎐0.067.
19
9.21" 4.42
Ž2.57᎐16.7.
17
1988
138 " 87.8
Ž10.8᎐307.
20
1.61" 1.02
Ž0.30᎐3.40.
17
0.06 " 0.06
Ž0.01᎐0.24.
18
0.122" 0.056
Ž0.049᎐0.268.
20
0.005" 0.005
Ž0.001᎐0.023.
20
10.6" 4.51
Ž2.57᎐16.3.
16
1989
124 " 67.1
Ž1.60᎐255.
21
1.00" 0.91
Ž0.50᎐4.09.
21
0.05" 0.05
Ž0.01᎐0.19.
21
0.152" 0.072
Ž0.054᎐0.283.
21
0.011" 0.010
Ž0.001᎐0.037.
21
12.5" 6.47
Ž2.02᎐25.0.
21
1990
131 " 119
Ž6.30᎐502.
22
0.77" 0.54
Ž0.50᎐2.30.
20
0.09" 0.15
Ž0.02᎐0.72.
21
0.180" 0.123
Ž0.053᎐0.472.
22
0.032" 0.034
Ž0.002᎐0.127.
22
9.83" 6.70
Ž0.08᎐19.5.
19
1991
169 " 126
Ž27.0᎐533.
22
0.75" 0.86
Ž0.10᎐2.82.
22
0.06" 0.09
Ž0.01᎐0.30.
22
0.198" 0.122
Ž0.069᎐0.466.
22
0.015" 0.019
Ž0.001᎐0.090.
22
12.1" 8.13
Ž0.20᎐23.7.
22
1992
187 " 106
Ž15.6᎐364.
20
0.82" 0.97
Ž0.20᎐3.23.
20
0.06" 0.11
Ž0.02᎐0.47.
20
0.183" 0.096
Ž0.054᎐0.386.
20
0.007" 0.004
Ž0.004᎐0.017.
20
9.29" 5.68
Ž1.07᎐18.4.
19
1993
143 " 133
Ž18.4᎐542.
20
1.10" 0.88
Ž0.50᎐2.75.
20
0.13" 0.19
Ž0.02᎐0.62.
20
0.150 " 0.067
Ž0.066᎐0.267.
20
0.011" 0.010
Ž0.004᎐0.040.
20
12.5" 6.31
Ž0.16᎐21.4.
20
a
Data are mean " S.D.
Bracket values are the range of variation.
c
Denotes the number of samples.
b
S.S.S. Lau, S.N. Lane r The Science of the Total En¨ ironment 288 (2002) 167᎐181
172
Table 3
Seasonal variation of chlorophyll a Ž␮g ly1 . and nutrients Žmg ly1 . in Barton Broad in 1983᎐1993
Chl a
a
TON
NH4
TP
SRP
Si
Winter
31.2" 26.2
Ž1.60᎐116.b
32c
2.57" 0.79
Ž0.79᎐4.40.
30
0.20" 0.15
Ž0.01᎐0.54.
31
0.081" 0.036
Ž0.028᎐0.211.
32
0.024" 0.030
Ž0.001᎐0.153.
32
15.0" 2.61
Ž5.99᎐19.7.
29
Spring
112 " 56.0
Ž27.0᎐272.
64
1.46" 1.54
Ž0.20᎐10.2.
55
0.04" 0.05
Ž0.01᎐0.29.
58
0.112" 0.042
Ž0.028᎐0.205.
64
0.009" 0.026
Ž0.000᎐0.180.
62
4.81" 4.03
Ž0.08᎐13.1.
56
Summer
173 " 106
Ž6.30᎐542.
79
0.57" 0.61
Ž0.10᎐3.40.
70
0.07" 0.13
Ž0.01᎐0.72.
79
0.205" 0.085
Ž0.080᎐0.472.
77
0.017" 0.022
Ž0.000᎐0.090.
79
9.76" 6.79
Ž0.02᎐25.0.
72
Autumn
147 " 81.3
Ž18.0᎐375.
55
0.77" 0.56
Ž0.10᎐2.20.
51
0.07" 0.11
Ž0.01᎐0.62.
53
0.158" 0.088
Ž0.062᎐0.466.
54
0.013" 0.022
Ž0.000᎐0.127.
52
11.5" 5.57
Ž0.21᎐20.3.
49
a
Data are mean " S.D.
Bracket values are the range of variation.
c
Denotes the number of samples.
b
variables including TON, NH 4 , TP, SRP, Si, Dap
and Zoo, were used for FA. Non-normality of the
data was treated by taking a logarithm or square
root, whenever appropriate ŽHair et al., 1998.. A
correlation matrix of these variables was computed and factor loadings were determined to
explore the nature of variation and principal patterns amongst them ŽReyment and Joreskog,
1993.. Any factor with an eigenvalue greater than
unity was selected as significant ŽCattell, 1978..
For interpretation purposes, the first two most
significant factor axes were portrayed as biplots,
and the loadings of all significant factor axes Ži.e.
eigenvalue ) 1. were tabulated. The eigenvalues,
which give the variance of the factor components,
were used as criteria for determining significance.
For easier interpretation, factor axes are modified
by ‘factor rotation’ using varimax to maximise the
total variance of the projections of the points on
the factor axes.
To evaluate relationships between these environmental variables and phytoplankton biomass,
a backward multiple regression analysis was computed using phytoplankton biomass Ži.e. chlorophyll a. as the dependent variable, and the factors obtained by FA as the independent variables,
in order to examine the controlling role of any
particular parameter on phytoplankton biomass.
The factors are orthogonal, so avoiding problems
of collinearity in regression analysis. The method
consists of starting with all independent factor
scores and then removing insignificant Ž P ) 0.05.
factor scores one at a time until the remaining
factor scores each explained a significant proportion of the total variance in the data set. It is
recognised that relationships identified by this
statistical approach do not necessarily signify
cause and effect but the multivariate method can
be a useful explanatory aid to the interpretation
of ecological data ŽLudwig and Reynolds, 1988..
Criteria for significance were set at P - 0.05.
Table 4 shows a matrix of Pearson’s correlation
coefficients between all the variables.
3. Results
3.1. Long-term patterns of phytoplankton biomass
and nutrient ¨ ariables
During the 11-year period Ž1983᎐1993., there
were 257 sampling dates, of which 153 were com-
S.S.S. Lau, S.N. Lane r The Science of the Total En¨ ironment 288 (2002) 167᎐181
173
Table 4
Correlation coefficient matrix
Entire period
Winter
Spring
Summer
Autumn
a
b
Chl a
Dap
NH4
Si
Dap
NH4
Si
SRP
TON
TP
Zoo
y0.105
y0.441b
y0.109
y0.109
y0.453b
0.713b
0.079
0.118
y0.106
0.091
y0.158a
0.026
0.500b
0.253b
0.182b
0.264b
y0.277b
y0.037
0.217b
0.125
0.154a
y0.324b
0.003
0.178b
0.041
Dap
NH4
Si
SRP
TON
TP
Zoo
0.486
y0.378a
y0.182
y0.324
y0.264
y0.053
y0.155
y0.376
y0.277
y0.331
0.012
y0.062
y0.126
0.213
y0.070
0.044
y0.033
0.265
y0.029
y0.382a
y0.016
y0.202
y0.177
0.794b
0.035
y0.276
y0.094
Dap
NH4
Si
SRP
TON
TP
Zoo
0.404b
y0.322a
y0.678b
y0.154
y0.170
0.548b
0.016
y0.086
y0.431b
y0.137
y0.344a
0.372b
0.325a
0.361b
0.199
0.007
y0.155
0.223
0.329a
0.317a
y0.542b
0.115
0.039
y0.260a
y0.018
y0.464b
y0.096
0.164
Dap
NH4
Si
SRP
TON
TP
Zoo
y0.434b
y0.370b
0.160
y0.037
y0.254a
0.688b
y0.291a
0.309b
y0.244a
0.163
0.028
y0.347b
0.523b
y0.064
0.286a
0.151
y0.240b
0.248a
y0.011
y0.015
0.366b
y0.163
y0.118
0.189
0.213
y0.285b
y0.044
y0.146
Dap
NH4
Si
SRP
TON
TP
Zoo
0.288a
y0.488b
y0.198
y0.136
y0.585b
0.618b
y0.069
y0.271
y0.493b
y0.189
y0.238
0.030
0.427b
0.234
0.115
0.446b
y0.295a
y0.091
0.457b
0.158
0.248
y0.318a
0.065
0.475b
y0.218
y0.450b
0.090
y0.228
SRP
TON
y0.496b
y0.294b
TP
0.133
y0.162
P- 0.01.
P- 0.05.
plete and 104 lacked some data. Table 2 shows
the annual mean and S.D. of selected key environmental parameters. Concentrations of chlorophyll a and different nutrient variables over the
study period were highly variable. The mean value
of annual standing crop over the period was 130
␮g Chl a ly1 , with the lowest value of 91.1 ␮g Chl
a ly1 in 1983 and the highest value of 187 ␮g Chl
a ly1 in 1992. Following a standard trophic level
classification ŽOrganisation for Economic Co-operation Development, 1982., 27% of years would
be classed as eutrophic ŽTP 40᎐100 ␮g ly1 . and
73% would be classed as hypertrophic ŽTP ) 100
␮g ly1 .. With regard to long-term inter-annual
variations, there were no significant negative
trends in TP and SRP ŽFig. 2., although a 90%
174
S.S.S. Lau, S.N. Lane r The Science of the Total En¨ ironment 288 (2002) 167᎐181
Fig. 2. Time series data in 1983᎐1993 of: Ža. SRP; and Žb. TP.
reduction of external P loading has been achieved
ŽOsborne, 1981; Phillips and Chilvers, 1991.. This
is reflected in no significant reduction in chlorophyll a concentration ŽFig. 3.. The percentage
reduction of external P loading was well above
the criterion reported by other researchers for
reduction in chlorophyll a concentration and an
associated increase of water transparency: it is
thought that a change in trophic category requires a 60᎐80% reduction in external TP loading
ŽOrganisation for Economic Co-operation Development, 1982; Forsberg, 1985..
The water chemistry also revealed that the lake
had a high level of oxidised nitrogen species; the
mean TON concentration was 1.15 mg ly1 over
the 11-year period Žranging from 0.75 mg ly1 in
S.S.S. Lau, S.N. Lane r The Science of the Total En¨ ironment 288 (2002) 167᎐181
175
Fig. 3. Time series data in 1983᎐1993 of Chl a.
1991 to 2.08 mg ly1 in 1985., but a relatively low
average NH 4 concentration of 0.08 mg ly1 over
the period was observed, with a minimum of 0.03
mg ly1 in 1984 and a maximum of 0.14 mg ly1 in
1987. Mean Si concentration over the period was
as high as 9.56 mg ly1 , with a minimum of 5.14
mg ly1 in 1984 and a maximum of 12.5 mg ly1 in
both 1989 and 1993.
3.2. Seasonal patterns of limiting nutrients
With reference to seasonal periodicity of phytoplankton biomass over the 11-year period ŽTable 3., the highest concentration was obtained in
summer with a mean of 173 ␮g Chl a ly1 , followed in a descending order by 147 ␮g Chl a ly1
in autumn, 112 ␮g Chl a ly1 in spring, and 31.2
␮g Chl a ly1 in winter. This seasonal trend
coincided with those of mean TP concentrations
of 0.205, 0.158, 0.112 and 0.081 ␮g ly1 , respectively. It appears to suggest that phytoplankton
biomass increases in response to increasing TP
concentration. This indicates possibly a close interacting relationship between Chl a and TP in
terms of resource limitation ŽLund and Reynolds,
1982., as was indicated by a significant correlation
Ž R 2 s 0.47, P- 0.001..
3.3. Forcing ¨ ariables and phytoplankton dynamics
Fig. 4. Biplot of the first two axis scores of factor analysis over
the 11-year period Žws winter; ps spring; s s summer; a s
autumn..
Owing to possible temporal variation in eutrophication, the pooling of all data from the
entire 11-year period to examine forcing variables
in relation to phytoplankton biomass may be
problematic; it may have weakened the effect of
any particular variable in a particular time period
176
S.S.S. Lau, S.N. Lane r The Science of the Total En¨ ironment 288 (2002) 167᎐181
that may otherwise have a dominating effect. In
particular, grazing and nutrient limitation of phytoplankton are seasonal-dependent Že.g. Vanni
and Temte, 1990.. Abdul-Hussein and Mason
Ž1988. showed that temperature differences may
be associated with 67% of the variation in phytoplankton biomass. Thus, it is important to explore
eutrophication variability in terms of seasonal
changes.
spring ŽMarch᎐May., summer ŽJune᎐August., and
autumn ŽSeptember᎐November. for the first two
axes. The classification matrix showed that 54%
of all data were correctly classified according to
the four seasons, indicating seasonal variability.
Factor and regression analyses were performed
separately for each season.
3.4. Eutrophication ¨ ariability in relation to seasonal
change
Factor analysis of winter data Žcompleted cases
used s 14, missing cases s 19. showed that the
eigenvalues for first two factors were 2.7 and 1.7,
respectively. Table 5 presents a summary of factor
loadings. F-1 and F-2 accounted for 54% of the
total variance and their loadings were plotted in a
bi-plot ŽFig. 5a.. Both factors were associated
with both biotic ŽDap. and abiotic ŽTP, TON and
Si. variables. F-1 which captured 28% of the total
variation, was related to the major nutrient ŽTP.
Given that temporal variations of phytoplankton biomass shifted in tandem with ambient environmental parameters, a linear discriminant analysis was performed to determine if the differences
between the assigned four seasons were significant. Fig. 4 shows the distribution of the score
coefficients of winter ŽDecember᎐February.,
3.5. Winter
Fig. 5. Plot of factor loadings for each variables of the first two factor axes of: Ža. winter data; Žb. spring data; Žc. summer data; and
Žd. autumn data.
S.S.S. Lau, S.N. Lane r The Science of the Total En¨ ironment 288 (2002) 167᎐181
Table 5
Factor loading matrix for phytoplankton-related variables
after a varimax rotation
Winter
Spring
Summer
Autumn
Variable
Factor 1
Factor 2
TP
Dap
TON
Si
Zoo
SRP
NH4
0.931
y0.742
0.081
0.154
0.037
0.489
0.533
0.175
0.094
y0.947
0.895
y0.084
y0.130
0.141
TON
TP
Si
Zoo
Dap
NH4
SRP
y0.906
0.816
y0.805
0.589
0.019
y0.003
0.002
y0.071
0.121
0.129
0.508
0.916
0.216
0.047
Zoo
Dap
TON
NH4
Si
TP
SRP
0.907
0.863
y0.032
0.071
y0.112
y0.184
0.018
y0.068
0.173
0.783
0.748
0.122
y0.590
y0.034
TON
TP
NH4
Si
Zoo
SRP
Dap
0.893
y0.824
0.673
0.024
y0.219
y0.003
0.082
0.006
0.080
y0.505
y0.804
0.726
y0.294
0.142
and grazing ŽDap. factors associated with eutrophication. F-2 accounted for 26% of the total
variation and was related to other nutrient variables ŽTON and Si.. Although TP, Dap, TON and
Si were all selected by FA, regression analysis did
not show any significant relationship Ž P) 0.05.
between the associated factors and phytoplankton
biomass.
3.6. Spring
In spring Žcompleted data cases s 39, missing
cases s 33., 52% of the total variation was accounted for by two factors, the eigenvalues were
2.6 for axis 1 and 1.6 for axis 2. The loading
177
matrix is shown in Table 5, and their loadings
plotted on a bi-plot in Fig. 5b. The first axis
accounted for 35% and the second for 17% of the
variance. F-1 was related to nutrient ŽTON, TP
and Si. availability. F-2 was related to predation
ŽDap. factor. To examine the relative effect of the
two factors in governing the phytoplankton biomass, a stepwise regression analysis was computed.
S - Chl as y 0.135q 0.312= F- 1
Ž adj. R 2 s 25%, S s 0.520.
Ž1.
This illustrates only F-1 was important in governing phytoplankton biomass. According to the
positive relationship between F-1 and TP, and
negative relationship between F-1 and TON and
Si, phytoplankton biomass as indicated by standardised Chl a increases with increasing TP and
with reducing TON and Si. It suggested that
phytoplankton was TP-limited in spring.
3.7. Summer
During the summer period Žcompleted data
cases s 59, missing cases s 36., eigenvalues of the
two factors were 2.1 for F-1 and 1.4 for F-2. The
loading matrix and bi-plot are given in Table 5
and Fig. 5c, respectively. The two factors captured
a relatively low level of the total variance Ž45%..
F-1 which explained 23% of the variance, was
related to a predatory ŽZoo and Dap. factor. F-2
which accounted for 22% of the variance, was
related to nutrient ŽTON, NH 4 and TP. availability. To explore the relative role of the two factors
in explaining the phytoplankton biomass Žas indicated by standardised, square root Chl a., a stepwise regression analysis was computed which
showed that both F-1 and F-2 were important in
the control of phytoplankton abundance.
S y 'Chl - a s 0.585y 0.374= F- 1
y 0.554= F- 2
Ž adj. R 2 s 46%, S s 0.707.
Ž2.
178
S.S.S. Lau, S.N. Lane r The Science of the Total En¨ ironment 288 (2002) 167᎐181
This regression result illustrates the role of
grazing pressure ŽZoo and Dap. and nutrient
resource ŽTON, NH 4 and TP. in relation to phytoplankton biomass and demonstrates a high connectivity between N, P, Zoo, Dap and phytoplankton. Considering the negative relationship
between phytoplankton and F-1 and F-2 in Eq.
Ž2., and the negative relationship between F-2
and TP and the positive relation between F-2 and
Zoo, Dap, TON and NH 4 ŽTable 5., phytoplankton was positively correlated to TP, but negatively
to Zoo, Dap, TON and NH 4 . Hence, nutrient Ži.e.
TP. and grazing Ži.e. Zoo and Dap. factors were
both limiting phytoplankton biomass in summer.
3.8. Autumn
Factor analysis of data from the autumn Žcompleted cases s 39, missing cases s 18. showed that
the first two factors Žwith eigenvalues of 2.6 for
F-1 and 1.9 for F-2., accounted for 50% of the
total variation. F-1 explained 28% of the variance
and was correlated with variables associated with
nutrient availability, including nitrogen ŽTON and
NH 4 . and phosphorus ŽTP. ŽTable 5, Fig. 5d..
Conversely, F-2 accounted for 22% of the variance and was related to both biotic ŽZoo. and
abiotic ŽSi. factors. To examine the importance of
the two factors in explaining phytoplankton biomass, a stepwise regression analysis was performed.
S - Chl as 0.247y 0.742= F- 1 q 0.305= F- 2
Ž3.
Ž adj. R 2 s 64%, S s 0.590.
The regression calculation suggests that phytoplankton biomass was controlled both by nutrient
and predator availability. Table 5 shows the interaction between the first two factors and their
associated variables: F-1 was positively related to
TON and NH 4 and negatively to TP; and F-2 was
positively correlated to Zoo and negatively to Si.
Thus, according to Eq. Ž3. and Table 5, phytoplankton biomass was positively associated with
TP and Zoo, and negatively with TON, NH 4 and
Si. This suggests that the interaction between
nutrients ŽN, P, Si., grazers ŽZoo. and phytoplankton was complex.
4. Discussion: the role of nutrients and
zooplankton in phytoplankton variability
Sophisticated statistical techniques may often
find significant relationships in a large data set
whether or not cause and effect actually occur
ŽLuoma and Bryan, 1982; Gilbert, 1987.. However, when a process in nature is controlled by a
suite of inter-relating variables, specifically focused statistical searches may be the only way to
discern the relative importance of the different
variables in the natural system ŽLuoma and Bryan,
1982.. In this study, we have statistically tested
the effects of seven variables on phytoplankton
biomass in shallow lake eutrophication. This study
sought to disentangle variable abiotic᎐biotic interactions in relation to phytoplankton biomass
and hence, consider the relative importance of
top-down and bottom-up controls in contributing
to long-term eutrophication in relation to seasonal periodicity. Despite the complexity of the
interactions between nutrient availability, phytoplankton biomass and zooplankton grazing, the
results showed that it is possible to disentangle
environmental processes causing observed patterns in eutrophication using multivariate statistical analysis on a long-term data set.
With a very high reduction Ži.e. 90%. of external P loading, it was found that the high in-lake
total phosphorus concentration was maintained
throughout the 11-year period. This observation
supports the notion that it is difficult to restore a
eutrophic lake solely by nutrient reduction. The
uncoupling of external P reduction and in-lake
chlorophyll a reduction possibly demonstrates
complex ecosystem behaviour in the context of
the restoration of Barton Broad, as compared to
the simple linear relationship that has been
observed elsewhere. The uncoupling may also be
associated with the error involved using percentage reductions of nutrient loading rather than
absolute in-lake total phosphorus values in determination of the nutrient ᎐phytoplankton relationships. On the other hand, the reason for the
uncoupling might be due to the potential influences of other environmental factors, such as
nitrogen and reactive dissolved silicon, upon phytoplankton biomass.
S.S.S. Lau, S.N. Lane r The Science of the Total En¨ ironment 288 (2002) 167᎐181
To examine the effect of particular environmental variables upon phytoplankton, Table 6
summarises the positive and negative interactions
between phytoplankton biomass and the associated abiotic and biotic variables. The statistical
evidence suggests that there is a strong temporal
heterogeneity in controls upon phytoplankton
biomass. In particular, the controlling factors influencing phytoplankton biomass varied from season to season. In summary: phytoplankton were
regulated by phosphorus, nitrogen and silicon in
spring; phosphorus, nitrogen and zooplankton in
summer; and phosphorus, nitrogen, silicon and
zooplankton in autumn. The environmental variables affecting phytoplankton biomass in winter
were not evident in the statistical relationship.
High inherent variability in the data set, a common characteristic of environmental data, may
explain the absence of any significant relationship
between phytoplankton biomass and the variables
in winter. In spring, summer and autumn, for
which significant regression results were obtained,
the relative significance of the suite of nutrient
and grazing factors in the control of phytoplankton biomass varied remarkably. In addition, the
results showed that more variables show a relation with phytoplankton in summer and autumn
than in spring. This indicates that phytoplankton
biomass may be sensitive and responsive to environmental variability in summer and autumn
weather as compared to any other season. Despite the seasonal variations, the critical environmental variable associated with variability of phytoplankton biomass would seem to be total phosphorus ŽTable 6.. Taking grazing effects into the
consideration, the limiting role of total zooplankton and Daphnia was present in the summer, but
not in other seasons, suggesting a shift of the
predatory role of the grazers between seasons
ŽLampert et al., 1986.. The grazing effect observed
in this study is in line with the finding that grazing
by zooplankton can prevent large algal populations from developing even in extremely fertile
water Že.g. Timms and Moss, 1984.. The present
study demonstrates that trophic inter-relationships in eutrophic lakes are strongly related to
temporal heterogeneity. In particular, seasonality
was identified as an important variable in affect-
179
Table 6
Phytoplankton and the associated regulatory variables
Relation to phytoplankton
Positive
Negative
Spring
TP
TON
Si
Summer
TP
Total zooplankton
Daphnia
TON
NH4
Autumn
TP
Total zooplankton
TON
NH4
Si
ing lake eutrophication. Given that changes in
seasonality Žas shown by varying environmental
variables. could strongly affect phytoplankton
variability, the question emerges as to whether
shifts in climatic parameters within a single season will influence eutrophication. This has yet to
be specifically tested. Conclusive evidence of the
dominant role of total phosphorus upon phytoplankton variability compared to other environmental variables, such as temperature and rainfall
in any single season must await further investigation. In conclusion, the study offers some optimism that phytoplankton biomass in eutrophic
shallow lakes can generally be predicted and explained by an array of abiotic and biotic variables,
despite the complexity of the environmental factors involved.
Acknowledgements
This research was conducted with the financial
support of the Croucher Foundation ŽHong Kong..
SSSL was in receipt of a Croucher PhD scholarship. This paper would not have been written
without the technical assistance of Sarah Caswell,
Claire Metcalfe and other staff of the Environment Agency ŽUK. in terms of data assimilation
whilst SSSL visited the Haddiscoe Laboratory in
spring 1999. We are grateful to Dr Geoff Phillips
and Dr Jo Pitt of the Environment Agency ŽUK.
for constructive comments on an early draft. The
180
S.S.S. Lau, S.N. Lane r The Science of the Total En¨ ironment 288 (2002) 167᎐181
manuscript benefited significantly from the comments of Prof. Brian Moss of University of Liverpool, UK.
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