Artigo

1. Hydrodynamics-driven plankton community in a shallow
lake
Luciana de Souza Cardoso Æ David da Motta Marques
Received: 27 February 2007 / Accepted: 1 November 2007 / Published online: 27 November 2007
Springer Science+Business Media B.V. 2007
Abstract Canonical correspondence analysis (CCA)
was used to test the hypothesis that the wind-governed
hydrodynamics of a shallow coastal lake is responsible
for the spatial and temporal gradients of biotic and
abiotic variables. Certain environmental variables,
such as turbidity, suspended solids, and water level,
formed seasonal spatial gradients in Itapeva Lake,
southern Brazil, in response to wind action. Physical
variables formed gradients more easily than did most
of the plankton community, although the densities of
certain species did respond to wind-driven oscillations.
The results of this analysis indicate that the spatial and
temporal gradients experienced by the physical,
chemical, and biological descriptors displayed a char-acteristic
property of this type of wind-driven
environment. Moreover, CCA revealed that water
dynamics may govern the plankton community of
Itapeva Lake.
Keywords Brazil Phytoplankton
Subtropical Water level Wind
Zooplankton
Introduction
Studies aimed at analyzing the link between hydro-dynamics
and biological processes are one approach
to gaining an understanding of aquatic ecosystems
(Legendre and Demers 1984), especially those of
shallow lakes where wind plays a major role (Lacroix
and Lescher-Moutoue´ 1995; Cardoso and Motta
Marques 2003, 2004a,b,c). Hydrodynamic processes
and biological changes occur over different spatial
and temporal scales and, consequently, any study of
the former requires consideration of the latter as well
as of the sampling scale and the interaction between
the physical and biological scales (Legendre and
Demers 1984; Pinel-Alloul 1995). Coupling between
abiotic and biotic processes has been discussed in the
context of the ‘‘multiple driving forces hypothesis’’.
This hypothesis confirms the primacy of abiotic
factors in the models of environmental control of
zooplankton spatial heterogeneity at large spatial
scales and suggests that at smaller scales, biological
processes are more important (Pinel-Alloul 1995). An
accurate sampling design (spatial scale) is critical in
analyzing the patterns of zooplankton distribution in
temperate lakes (Lacroix and Lescher-Moutoue´ 1995;
Pinel-Alloul 1995; Pinel-Alloul et al. 1999; Thack-eray
et al. 2004). Several studies have shown that
wind-induced water movements have a dominant
effect on basin-scale distribution patterns. However,
few attempts have been made to quantify the effect of
physical processes on these broad-scale patterns
L. de Souza Cardoso ()
Instituto de Biocieˆncias, UFRGS, Porto Alegre, RS CEP
91501–970, Brazil
e-mail: luciana.cardoso@ufrgs.br
D. da Motta Marques
Instituto de Pesquisas Hidra´ulicas, IPH-UFRGS, Cx. P.
15029, Porto Alegre, RS CEP 91501–970, Brazil
e-mail: dmm@iph.ufrgs.br
123
Aquat Ecol (2009) 43:73–84
DOI 10.1007/s10452-007-9151-x

2. 74 Aquat Ecol (2009) 43:73–84
(Thackeray et al. 2004). The area of hydrodynamics
remains a field for numerical modeling with simula-tions
of the biological dynamics, especially in
reservoirs (Bruce et al. 2006). Nonetheless, the initial
hypotheses are still valid, mainly for shallow lakes.
Planktonic organisms are known for their potential
as bioindicators. The choice of an appropriate, simple
method for establishing the relation between biotic and
abiotic factors is of fundamental importance in eval-uating
changes in a plankton community. Several
methods in the field of multivariate statistics have been
employed toward this objective (Jongman et al. 1987),
however analytical methods are worthless without an
appropriate sampling scheme. Canonical correspon-dence
analysis (CCA) is a gradient analysis method
that has rapidly come into wide use in ecological
studies and in the analysis of shallow lakes, for both
phytoplankton (Agbeti et al. 1997; Flores and Barone
1998; Havens et al. 1998; Izaguirre et al. 2004; Tell
et al. 2005) and zooplankton (Pinel-Alloul et al. 1995;
Agbeti et al. 1997; Attayde and Bozelli 1998; Antunes
et al. 2003). In these studies, the species–environment
CCA correlations have shown spatial (Pinel-Alloul
et al. 1995; Attayde and Bozelli 1998; Izaguirre et al.
2004; Tell et al. 2005) or temporal variability (Flores
and Barone 1998; Havens et al. 1998; Antunes et al.
2003), but no consideration was given to the hydrody-namic
aspects.
Silva et al. (2005) recently used CCA to establish
correlations between phytoplankton community struc-ture
and hydrodynamic pattern in reservoirs. In this
study, the first CCA axis reflected the temporal
distinction between the sampling months, and the
segregation was determined to be due to high concen-trations
of suspended matter and higher water
temperature. The second axis separated the five
cascading reservoirs spatially (Silva et al. 2005). In
reservoirs, the relation between hydrodynamic aspects
(e.g., hydraulic stability) and plankton community are
not well understood. Studies of the physical driving
forces in shallow lakes are therefore becoming more
frequent in attempts to explain the spatial and/or
temporal variations of the plankton community.
Hydrodynamic variables may, in some situations,
control the plankton community, not only in reservoirs
but also in shallow lakes (e.g., Cardoso and Motta
Marques 2003, 2004a,b,c).
The main objective of the study reported here was
to determine the short-term patterns derived from the
interactions of wind-driven hydrodynamics and the
plankton community in a large, shallow lake. Our
hypothesis is that short-term patterns can be statisti-cally
demonstrated using CCA in the appropriate
spatial and temporal scales.
Materials and methods
Itapeva Lake is the first and northernmost lake in a
system of interconnected freshwater coastal lakes
located on the northern coast of the state of
Rio Grande do Sul, Brazil. It is elongated (30.8 9
7.6 km), with a surface area of approximately
125 km2 and shallow, with a maximum depth of
2.5 m (Fig. 1); its longest axis is aligned with the
prevailing winds (Cardoso and Motta Marques 2003).
The hydrodynamic pattern of the lake was modeled
by Lopardo (2002), whose first measures of
hydrometeorologic data indicated fast changes due
to wind gusts in which seiches were generated at
north and south sites (Lopardo 2002; Cardoso and
Motta Marques 2003). Simulations using a mathe-matical
two-dimensional horizontal hydrodynamic
model (IPH-A: http://www.iph.ufrgs.br) reproduced
this phenomenon, thereby facilitating an estimation
of the velocity and direction of the water current.
These latter two hydrodynamic variables were found
to explain 70 and 95% of the observed variation in
suspended solids and turbidity, respectively, in
each sampling season, based on averaged values of
4 h-periods (Lopardo 2002). The analysis of the
current also enabled the variations in water level
caused by seiches to be evaluated (average of
22 cm day-1). The hydrodynamic variables showed a
Fig. 1 Study area with sampling stations on Itapeva Lake
123

3. Aquat Ecol (2009) 43:73–84 75
characteristic seasonal behavior at each sampling
location that were closely related to wind velocity
and direction. Itapeva Lake’s hydrodynamic behavior
is well-defined; the central area is a transition zone
between the shoreline areas and at times has flow
patterns similar to either the southern or northern
area, dependent on the wind direction (Cardoso et al.
2003).
Instrumentswere installed on metal towers located in
three sampling areas in the lake (north, central, and
south): a water-level gauge, a DAVISWeather Wizard
III + Weather Link weather station (only on the tower
in the central area; wind direction and velocity, air
temperature, precipitation), and a YSI 6000 multiprobe
(water temperature, pH, conductivity, dissolved oxygen
saturation percentage, oxidation-reduction potential,
turbidity). The data were collected at a sub-surface
depth automatically at regular high-frequency intervals
(every 15 min for water-level data, every 30 min for
meteorological data, and every 5 min for multiprobe
data).Water velocity and directionwere obtained as part
of the output of the mathematical model for the lake
(Lopardo 2002).
Sub-surface water samples for phytoplankton and
zooplankton – species and density analyses – and
chemical data were collected in consecutive 4 h-intervals
throughout the day (0600, 1000, 1400 and
1800 hours) over three sampling days and over four
seasonal profiles [December 1998 (spring), March
1999 (summer), May 1999 (autumn), and August
1999 (winter); Cardoso and Motta Marques 2004a,c].
The fetch, wind, and physical and chemical charac-teristics
of Itapeva Lake have been described by
Cardoso and Motta Marques (2003) and Cardoso
et al. (2003). A synthesis of these data is presented
for each seasonal sampling period (Table 1). The
nauplii life stages were lumped as a ‘‘taxon’’ in order
to perform the analysis because it is impossible to
identify the species in this stage.
The selected environmental variables were those
related to hydrodynamics (water level, water velocity,
meanwind velocity,wind direction) and those known to
affect plankton communities (temperature, suspended
solids, Kjeldahl total nitrogen, total phosphorus, and
turbidity). Planktonic community metabolism
(Vollenweider 1974; APHA 1992) was characterized
bymeasuring primary production and respiration, using
the oxygen method, and chlorophyll a (Cardoso and
MottaMarques 2002, 2004b) as phytoplankton biomass.
Table 1 Means and standard errors (SE) of the environmental variables measured during each season at Itapeva Lake
Temperature
(C)
Fetch
(km)
CHL a
(lg l-1)
Respiration
(mg cm-3
h-1)
PP
(mg c
m-3 h-1)
VH2O
(m s-1)
DIR () LEV
Seasons SS
(m)
VMED
(m s-1)
Turbidity
(NTU)
P
(mg l-1)
N
(mg l-1)
(mg l-1)
Mean 119.5 2.9 0.78 147.7 4.6 SW 1.3 38.8 107.8 53.4 8.6–19.8 22.5
SE 10.0 0.11 0.04 11.6 0.4 10.2 0.10 14.2 29.6 8.4 1.4
December
(spring)
Mean 35.6 1.5 0.48 96.1 5.5 NE 1.1 0.039 54.9 49.9 7.4 10.6–15.6 27.8
SE 5.0 0.10 0.04 8.5 0.6 5.1 0.02 0.005 21.2 22.5 0.8 1.2
March
(summer)
Mean 135.8 1.1 0.36 215.6 6.1 W-SW 1.6 0.032 39.8 131.1 7.8 5.7–14.0 14.5
SE 26.0 0.12 0.06 36.1 0.4 4.3 0.02 0.005 16.5 38.5 1.3 0.6
May
(autumn)
Mean 161.1 1.8 0.34 36.1 8.6 W-SW 1.3 0.055 48.6 57.8 12.4 5.7–14.0 13.6
SE 42.7 0.25 0.06 4.44 0.2 1.0 0.02 0.005 18.8 18.3 2.4 0.4
August
(winter)
SS, Suspended solids; N, Kjeldahl total nitrogen; P, total phosphorus; NTU, nephelometric turbidity units; VMED, mean wind velocity; DIR, wind direction; LEV, water level; VH2O, water
movement velocity; PP, primary production; CHL a, chlorophyll a
123

4. 76 Aquat Ecol (2009) 43:73–84
Canonical correspondence analysis was used to
statistically evaluate the data set (Ter Braak 1986).
Data matrices were matched in time and space
intervals. The density data matrix of the plankton
community included the abundant and dominant
species with a presence frequency above 50% of the
sampling design (n = 36 samples in each seasonal
period). The CCA results were plotted as the variables
significantly correlated with the respective axis. Den-sity
data for the plankton species and nauplii were log-transformed
using log10(x + 1) in order to normalize
the variances (Ter Braak 1986). Row (samples) and
column (species or environmental data) scores were
standardized by centering and normalizing. Scaling of
ordination scores was chosen as a compromise
between row (samples) and column (species). To
assess the significance of the ordination axis for
exploratory purposes, we carried out a Monte Carlo
permutation test. The significance of the CCA axis was
tested by running 999 unrestricted permutations using
the axis eigenvalues as statistical tests. Comparisons
betweenCCAordinations were quantified according to
the eigenvalue size and significance. The size of a
significant eigenvalue was examined as a measurement
of the information content. The r values for each
environmental variable were intra-set correlations (Ter
Braak 1986). Canonical correspondence analysis was
performed using PC ORD ver. 4.0 (McCune and
Mefford 1999). PC ORD does not allow the forward
selection of environmental variables as does
CANOCO. Consequently, CCA was run using
CANOCO software to confirm the importance of
environmental variables to be used in CCA. Redundant
variables were removed. The final analysis was carried
out using PC ORD because this program has a better
graphics resolution. One-way ANOVAs were applied
to test the significance of site and time on the CCA site
scores of each axis and used to determine the relative
contribution of space and time as well as whether both
or just one of the axes represent temporal and/or spatial
gradients. The full database (seasonal) was subjected to
a two-way ANOVA.
Results
The CCA main results for the first two canonical axes
are presented in Table 2 for short-term and seasonal
changes.
Short-term changes
Spring
The ordination reflected a significant spatial gradient in
spring (F = 5.11, df1 = 2, df2 = 8, P = 0.037 for
axis 1; F = 67.3, df1 = 2, df2 = 8, P0.001 for axis 2)
due to the species–environmental correlations for both
axes (Fig. 2). On axis 2, turbidity (r = -0.91) was
highlighted in spatial gradients, where the protist
Arcella rotundata Playfair, 1918 followed by the
copepod Notodiaptomus incompositus Brian, 1925
were the most abundant species in the central area.
Secondary suspended solids (r = 0.66) and total
nitrogen (r = 0.63) shaped the spatial distinction of
the central area on axis 1. Primary production
(r = 0.46) and respiration (r = 0.48) were important
in separating the northern area from the other areas, in
association with higher densities of the protist Codo-nella
sp. and the rotifer Keratella cochlearis Gosse´,
1851. Nauplii, the cyanobacteria Cyanodiction imper-fectum
Cromberg et Weibull, and the diatoms
Aulacoseira distans (Ehrenberg) Simonsen and
A. granulata (Ehrenberg) Simonsen were plotted near
the centroid because their densities were similar among
sites. The wind direction (r = 0.47) could be imposing
a soft temporal gradient because it changed during the
day, although the effect was not significant (F = 0.68
for axis 1 and F = 0.02 for axis 2; df1 = 3, df 2 = 7,
P[0.05 for both axes).
Summer
Ordination of the summer data revealed a spatial
gradient (F = 14.95, df1 = 2, df2 = 8, P = 0.002
for axis 1) that was largely attributable to the
species–environment correlations with axis 1 (Fig. 3).
The constant northeast (NE) wind in the summer
(March 1999) separated the southern from the central
and northern areas, resulting in some variables dis-playing
the effect of this constant wind. Turbidity
(r = -0.84), suspended solids (r = -0.68) and water
level (r = -0.56) were wind-related hydrodynamic
variables that were associated with the spatial gradients
(axis 1). The species plotted in the right-hand side of
the ordination (Difflugia tuberculata Wallich, 1864,
Polyarthra sp., Keratella cochlearis, and nauplii) were
abundant in the central area of the lake, although the
123

5. Aquat Ecol (2009) 43:73–84 77
Table 2 The main results of the canonical correspondence analysis (CCA) for each season separately and for all seasons at Itapeva
Lake
December (spring) March (summer) May (autumn) August (winter) Overall
Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2 Axis 1 Axis 2
Eigenvalue 0.015 0.012 0.01 0.003 0.03 0.002 0.019 0.003 0.443 0.123
Percentage of variance explained 47.1 37.8 64.5 18.9 90.7 7.2 76.3 12 56.7 15.7
Cumulative % explained 47.1 84.9 64.5 83.4 90.7 97.9 76.3 88.4 56.7 72.4
Inertia (total variance) 0.032 0.015 0.033 0.024 0.782
Pearson correlation (r) 0.999 0.999 0.999 0.999 0.999 0.999 0.999 0.999 0.911 0.915
P (Monte Carlo) 0.001 0.002 0.002 0.016 0.011 0.009 0.001 0.002 0.001 0.001
Intra-set correlations biplots
PP 0.36 0.46 0.58 -0.21 0.44 0.31
Respiration 0.43 0.48 0.67 -0.06
DIR 0.47 -0.06 -0.14 -0.54
SS 0.66 -0.59 -0.68 -0.58 0.86 -0.30 -0.92 0.25
N 0.63 -0.54 0.21 0.58 -0.81 0.12 0.61 -0.07
Turbidity 0.21 -0.91 -0.84 -0.14 0.85 -0.30 -0.95 0.20
Water level -0.56 -0.05 0.63 -0.16 -0.91 -0.08 -0.67 0.45
P -0.48 -0.28 0.84 -0.14 -0.91 0.14 0.44 -0.47
VMED -0.30 -0.48 0.18 0.62 -0.12 0.75
VH2O -0.05 0.46 -0.19 0.64
CHL a 0.63 -0.25 0.48 -0.22
Temperature 0.50 -0.75
highest density was recorded in the northern area at
10 am. Anabaena circinalis Rabenhorst ex Bonet et
Flahault and Codonella sp. were the dominant species
in the northern area, decreasing in density in the
southern area. Hydrodynamics played an important
role in the plankton transport in the lake and caused this
spatial heterogeneity. The species on the left side of the
ordination, Aulacoseira distans (highest density in the
southern area), A. granulata, and Cyanodiction imper-fectum
(abundant at all sites), were plotted near the
vectors of water level and turbidity, indicating that
their distribution was closely associated with these
variables.
There was a time split on axis 2. Nitrogen
concentrations were high in the morning (r = 0.58)
in the central and the southern areas, decreasing in
the afternoon. Temporal gradients that increased from
morning to afternoon were observed for mean wind
velocity in all areas (r = -0.48), wind direction
(r = -0.54), which greatly affected suspended solids
(r = -0.58), and phosphorus (r = -0.48). The tem-poral
gradients (between 4 h-intervals of sampling),
although not significant by ANOVA (F = 0.32 for
axis 1 and F = 2.42 for axis 2; df1 = 3, df 2 = 7,
P[0.05 for both axes), were reflected in the CCA
ordination. These temporal gradients were visible in
the central and southern areas (Fig. 3) because these
areas were most affected by the dynamics generated
by the constant winds from the NE quadrant (longest
fetch).
Autumn
Spatial gradients were also observed in the autumn
(F = 8.85, df1 = 2, df2 = 9, P = 0.007 for axis 1),
especially due to species–environmental correlations
on axis 1 (Fig. 4). An increase in water level (r = 0.63)
from south to north that was generated by wind action
(SW–W, 8.5 m s-1) at the beginning of the day may
have contributed directly to the transport of phyto-plankton
towards this region. However, temporal
variation was not confirmed by ANOVA (F = 0.44
for axis 1 and F = 1.66 for axis 2; df1 = 3, df2 = 8,
For definition of environmental variables, see footnote to Table 1
123

6. 78 Aquat Ecol (2009) 43:73–84
P[0.05 for both axes). Sheltered from the SW fetch
(19.6 km), the southern area was correspondingly less
dynamic and spatially separate. In the situation of the
SW fetch, the central area on axis 2 was strongly
influenced by wind (r = 0.62) and water (r = 0.46)
velocities. Thus, the spatial gradient (southern to
northern areas) was associated with increased chloro-phyll
a (r = 0.63) and primary production (r = 0.58)
in the northern area as well as suspended solids (r =
0.86), turbidity (r = 0.85), phosphorus (r = 0.84),
and respiration (r = 0.67). The spatial distribution of a
cyanobacteria bloom (Anabaena circinalis and
A. spiroides Klebahn) and Codonella sp. in the lake
from the southern to northern region during the autumn
can also be considered to result from the transport of
solids by water and wind. The bloom was more
prominent under the calm conditions in the southern
area whereas turbulent areas – the northern area with
the longest fetch – favored the protist Codonella sp.
However, the distribution of Polyarthra sp. and nauplii
were not influenced by the environmental variables
correlated in the ordination.
Winter
Only a spatial gradient (F = 52.4, df1 = 2, df 2 = 7,
P0.001 for axis 1) was observed, mainly because
N6
N14 N10 N18
C6
Ci
Ag
C10
PP
RESP
C14
S18
S14 S6
C18
Co
Ar
Kc
DIR
No
na
Ad
N
SS
TURB
-1.0
1.5
0.5
-1.5
0.0
-0.5
DEC / 98
Axis 1
Axis 2
1.0 2.0
Fig. 2 Canonical correspondence analysis ordination diagram
for species during the spring (DEC/98 December) at Itapeva
Lake at different sampling locations (N northern, C center, S
southern) and sampling times shifts (6 0600 hours, 10 1000
hours, 14 1400 hours, 18 1800 hours) in relation to the
environmental variables (PP primary production, RESP respi-ration,
DIR wind direction, SS suspended solids, N Kjeldahl
total nitrogen, TURB turbidity, Co Codonella sp., Kc Keratella
cochlearis, No Notodiaptomus incompositus, Ar Arcella rotun-data,
Ag Aulacoseira granulata, Ci Cyanodiction imperfectum,
Ad Aulacoseira distans, na nauplii)
N10
C6
C10
N14 N18
C14
S6
S10
C18
S18
S14
Co
Dt
Kc
Po
na
Ad Ag Ac
Ci
SS
N
TURB P
VMED
DIR
LEV
-1.0
0.4
0.0
-0.4
-1.2
0.0
-0.8
MAR / 99
Axis 1
Axis 2
1.0
Fig. 3 CCA ordination
diagram for species during
summer (MAR/99 March)
at Itapeva Lake at the
sampling locations (N, C, S)
and sampling time shifts
(6,10, 14, 18) in relation to
the environmental variables
(DIR, SS, N, TURB, LEV
water level, P, VMED mean
wind direction,
Co Codonella sp.,
Kc Keratella cochlearis,
Po Polyarthra spp.,
Dt Difflugia tuberculata,
Ag Aulacoseira granulata,
Ci Cyanodiction
imperfectum, Ac Anabaena
circinalis, Ad Aulacoseira
distans, na nauplii). For
definitions of other
abbreviations, see caption to
Fig. 2
123

7. Aquat Ecol (2009) 43:73–84 79
S18
0.4
0.0
of the species–environmental correlations for axis 1
(Fig. 5). The WSW winds and the associated 14.0 km
fetch produced a strong spatial gradient, clearly
separating the three areas in the lake. Turbidity
(r = -0.95), suspended solids (r = -0.92), and
water level (r = -0.91), all variables associated with
wind and water-dynamics, as well as phosphorus
(r = -0.91) and nitrogen (r = -0.81) displayed a
gradient that increased toward the northern area.
Codonella sp. and Polyarthra sp. were dominant and
abundant in the northern area, whereas Notodiapto-mus
incompositus was abundant in the southern area.
N6
N10
C6
C10
N14
N18
C18
C14
RESP
CHL
Anabaena circinalis remained especially in northern
area (10 am). In contrast, primary production
(r = 0.44) was higher in the south, thereby showing
an opposite gradient. The other phytoplankton spe-cies
were plotted near the primary production vector
since they are related to this variable.
Seasonal changes
In order to assess the expected seasonal separation, the
CCA was run using the full data set. The CCA
S6
S10
S14
Co
Po
na
Ac
As
SS
P
TURB
VMED
LEV
VH2O
PP
-1.0
-0.8
0.0
-0.4
MAY / 99
Axis 1
Axis 2
1.0
Fig. 4 CCA ordination
diagram for species during
the autumn (MAY/99 May)
at Itapeva Lake at the
sampling locations (N, C, S)
and sampling time shifts
(6,10, 14, 18) in relation to
the environmental variables
(PP, RESP, SS, TURB, LEV,
P, VMED, CHL chlorophyll
a, VH2O, water movement
velocity, Co Codonella sp.,
Po Polyarthra spp.,
Ac Anabaena circinalis,
As Anabaena spiroides,
na nauplii). For definitions
of other abbreviations, see
captions to Figs. 2 and 3
N6
N14
N18
TURB
N10
C6
C10
S6
S14
S10
S18
Co
Po
No
na
Ad
Ag
Ac
Ci Pl
SS
NP
LEV
PP
-1.5
0.4
0.0
-0.8
-0.5
-0.4
AUG / 99
Axis 1
Axis 2
0.5 1.5
Fig. 5 CCA ordination diagram for species during the winter
(AUG/99 August) at Itapeva Lake at the sampling locations (N,
C, S) and sampling time shifts (6,10, 14, 18) in relation to the
environmental variables (PP, SS, N, TURB, LEV, P, Co Codo-nella
sp., No Notodiaptomus incompositus, Po Polyarthra spp.,
Ag Aulacoseira granulata, Ci Cyanodiction imperfectum,
Ac Anabaena circinalis, Ad Aulacoseira distans, Pl Plank-tolyngbya
limnetica, na nauplii). For definitions of other
abbreviations, see captions to Figs. 2 and 3
123

8. 80 Aquat Ecol (2009) 43:73–84
ordination showed a strong temporal gradient
(F = 4508.2 for axis 1 and F = 1570.5 for axis 2;
df1 = 3, df2 = 40, P is nearly zero for both axes)
(Fig. 6). Water level (r = -0.67), wind velocity
(r = 0.75), and water velocity (r = 0.64) separated
the cold seasons (autumn and winter) from the others.
Temperature (r = –0.75), nitrogen (r = 0.61), chlo-rophyll
a (r = 0.48), and phosphorus (r = –0.47) were
more closely correlated with the warm seasons (spring
and summer) by showing a production component.
Eleven species were more closely correlated with a
specifically described variable. Thus, Planktolyngbya
limnetica (Lemmermann) Koma´rkova–Legnerova´
et Cronberg, the bloom of cyanobacteria (Anabaena
circinalis followed by A. spiroides), and the rotifer
Polyarthra sp. were characteristic of cold seasons, with
their densities decreasing with increased temperature,
nitrogen, phosphorus and chlorophyll a. The cyano-bacteria
Cyanodiction imperfectum and the diatoms
Aulacoseira distans and A. granulata were more
characteristic of warm seasons and absent only in the
autumn. The copepod Notodiaptomus incompositus
appeared to be adapted to transitional periods between
warm and cold seasons. In contrast, the rotifer Kera-tella
cochlearis occurred only in the warm seasons, and
the testaceans Arcella cf. rotundata and Difflugia
tuberculata occurred only in the spring and summer,
respectively. The wind-driven hydrodynamic variables
(water level and water velocity) correlated with
seasonal succession and played an important role in
separating the planktonic communities by CCA in
Itapeva Lake. Nauplii and Codonella sp. occurred
during all seasons, being abundant or dominant most of
the time; consequently, they were plotted near the
centroid. These two zooplankton organisms were
residents at Itapeva Lake independently of seasonal
succession or hydrodynamics. Codonella sp. was
considered to be adapted to changes in the food web,
surviving under turbid and turbulent conditions. The
same condition did not apply to nauplii because it is a
life stage of different copepods.
Spatial and temporal variance was seasonally
significant on axis 1 (F = 11103.3, df1 = 3, df 2 =
8, P0.001 for time, and F = 5.52, df1 = 2,
df 2 = 16, P = 0.015 for space) and axis 2 (F =
3443.9, df1 = 3, df 2 = 8, P0.001 for time, and
F = 7.25, df1 = 2, df 2 = 16, P = 0.006 for space)
as well as interaction between space and time
(F = 22.8, df1 = 6, df 2 = 16, P0.001, for axis
1, and F = 5.26, df1 = 6, df 2 = 16, P = 0.004, for
axis 2).
Discussion
The water level of Itapeva Lake was affected by wind
action in a very direct manner: NE winds displaced
water from the north to south, along the main axis of
the lake; winds from the SW produced the opposite
effect (Cardoso et al. 2003; Cardoso and Motta
Marques 2003, 2004a). Southwest and WSW winds
were characteristic of the cold seasons and affected
the dynamics of the lake more than the NE wind
(Figs. 3–5). Pinel-Alloul et al. (1999) reported that
zooplankton distribution in Lake Geneva (the largest
and deepest subalpine lake in Western Europe) may
be strongly influenced by variations in wind intensi-ties
between sheltered and exposed areas. Thus, wind
seems to play an important role in driving physical
VH2O
Co
Ar
No
Kc
na
Ad
Ag
Ci
Dt
Ac
Po
As
Pl
N
P
VMED
LEV
CHL
TEMP
-2.5
2.5
1.5
0.5
-1.5
-1.5
-0.5
Axis 1
Axis 2
Dec
Aug Mar
May
-0.5 0.5 1.5
Fig. 6 CCA ordination diagram for species during all seasonal
campaign [open triangle December (spring), open box March
(summer), open square May (autumn), open circle August
(winter)] at Itapeva Lake in relation to the environmental
variables (N, LEV, P, VMED, VH2O, CHL chlorophyll a,
TEMP temperature, Co Codonella sp., Kc Keratella cochle-aris,
No Notodiaptomus incompositus, Po Polyarthra spp.,
Ar Arcella rotundata, Dt Difflugia tuberculata, Ag Aulacose-ira
granulata, Ci Cyanodiction imperfectum, Ac Anabaena
circinalis, As Anabaena spiroides, Ad Aulacoseira distans,
Pl Planktolyngbya limnetica, na nauplii). For definitions of
other abbreviations, see captions to Figs. 2 and 3
123

9. Aquat Ecol (2009) 43:73–84 81
aspects in both deep and shallow lakes, whether
temperate, subtropical, or tropical.
Abiotic factors, such as wind, precipitation,
turbidity, and hydrology, are critical factors affecting
the seasonality of zooplankton in the tropics (Hart
1990; Mengistu and Fernando 1991). Dejen et al.
(2004) demonstrated the effect of these factors and of
chlorophyll a in structuring the zooplankton assem-blage
in a large tropical lake where CCA revealed
that zooplankton abundance correlated most strongly
with turbidity over seasons and space. Spatial hori-zontal
gradients in turbidity may also affect the
occurrence and distribution of zooplankton organisms
(Hart 1990), and wind-induced currents appeared to
be of major significance in determining the horizontal
distribution of the copepod in subtropical Lake
Sibaya (Hart 1978). In Itapeva Lake, temporal and
spatial turbidity gradients were associated with the
structure of the zooplankton and phytoplankton
communities. While wind-driven hydrodynamics
can affect water chemistry, planktonic metabolism,
and water level, one of its main effects is on the
turbidity of the water.
For phytoplankton in the Rı´mov Reservoir, CCA
analysis of basic environmental variables and entire
assemblages indicated a division into two large
groups: a cold assemblage in winter/spring and a
warm assemblage in summer/autumn (Koma´rkova´
et al. 2003). Temperature has been show to be
important in explaining temporal variations in
phytoplankton found in a shallow lake (Flores and
Barone 1998) as well as in a reservoir (Silva et al.
2005). However, temperature may not be the most
significant variable responsible for the spatial varia-tion
of phytoplankton in a subtropical shallow lake
(Izaguirre et al. 2004). In our study, hydrodynamic
variables, such as water level, water velocity, wind
velocity, and the consequent turbidity, distinguished
the cold seasons from the warm seasons (Fig. 6).
High phosphorus, nitrogen, chlorophyll a concentra-tions
and temperature characterized the warm seasons
(Fig. 6).
In Itapeva Lake, the variance explained by the first
CCA ordination axis ranged from 47.1 (spring) to
90.7% (autumn) and was highly significant
(P0.01). The first two biplot axes provided the
most information on the environmental gradients. It
was also possible to evaluate the significance of
indicator properties in relation to the relevant
environmental variables. For Itapeva Lake, the
significance was quite high (usually0.01), indicat-ing
a clear spatial and/or temporal gradient. This was
especially true on a short-term scale, with sampling
shifts in terms of hours (Figs. 2–5). Short-term
temporal gradients are more important than seasonal
gradients in terms of spatial scale ( Padisa´k et al.
1990; Vincent 1992; Carrick et al. 1993). This is to be
expected in shallow environments, which are likely to
experience frequent disturbances from wind-induced
hydrodynamic changes. In Itapeva Lake, these short-term
gradients were observed in the summer as a
result of changing wind direction, and in autumn, they
resulted from changes in the water level (Figs. 3, 4).
The spatial gradients were relatively constant, indi-cating
the existence of hydrodynamically derived
water compartments (Lopardo 2002). As expected,
seasonal separation was clear, although without a
spatial gradient in each season (Fig. 6). Because the
main objective of this study was to understand the
influence of hydrodynamics on the temporal and
spatial patterns of plankton structure in a shallow lake,
an appropriate sampling time scale, i.e., short-term,
was fundamental. Thus, only a high-frequency sam-pling
associated with a short-term analysis within
each season could identify spatial gradients patterns
(Figs. 2–5).
Differences between different sampling sites and
times are caused by the spatial heterogeneity of
plankton communities. In Itapeva Lake, this short-term
patchiness is caused by wind-driven movements
of the water and particles in suspension. Indeed, the
response time of phytoplankton in Itapeva Lake
(Cardoso and Motta Marques 2003) was very rapid,
being evident on a time scale of hours. A pattern of
abrupt shifts from one stable assemblage to another
was the result of intense disturbances caused by the
wind, and could occur at during an interval of a few
hours. The seasonal succession in the phytoplankton
community was more pronounced between the sum-mer
and autumn, when the sediment resuspension
events (caused by wind) were decisive in remineral-ization.
Resuspension renders diatoms dominant in
the system, and they are replaced by cyanobacteria
when conditions quieten down again (Cardoso and
Motta Marques 2003, 2004c).
In similar other shallow-water systems in the
world, mixing events have been shown to lead to
large increases in the phytoplankton production and
123

10. 82 Aquat Ecol (2009) 43:73–84
changes in the community composition. However, it
is not clear whether changes in the phytoplankton
biomass are related to pulses of sediment nutrient
release into the upper water layers during resuspen-sion
events or to direct inoculation of algae into the
water from the lake bottom. Some phytoplankton
seasonal cycles can be explained through water
column destratification related to wind turbulence,
which induces the meroplankton in the photic zone to
begin a new growth phase. It is also possible that
there is a biological adaptation to turbulent water
conditions (Carrick et al. 1993). The increase in
phytoplankton production and the change in community
structure associated with turbulence has been very
well studied in shallow lakes in Hungary (Padisa´k
et al. 1988, 1990; Padisa´k 1993; Dokulil and Padisa´k
1994; Padisa´k and Dokulil 1994). The succession of
phytoplankton biomass size fractions in Itapeva Lake
was directly related to disturbances caused by strong
winds and long fetches, or the lack of these (Becker
and Motta Marques 2004). In Itapeva Lake, there are
indications that observed changes in the plankton
community are related to the release of nutrients
(especially phosphorus) from the sediment into the
water column as a result of wind-driven hydrody-namics
(Cardoso 2001). The CCA analysis presented
here suggests that some aspects of planktonic
dynamics in Itapeva Lake are linked to suspended
matter which, in turn, is associated with wind-driven
hydrodynamics. Wind may reduce water transparency
through sediment resuspension, with internal regen-eration
of the nutrient pool. In shallow lakes, with the
depth varying from 1 to 3 m, Cristofor et al. (1994)
found that the critical intensity of the wind that
generated this turbulence varied from 3.2 to 5.4 m s-1.
The elongated shape of Itapeva Lake, which is parallel
to the main axis of the prevailing winds (NE–SW), and
the mean wind velocity (averaging 5.04 m s-1 in the
autumn, to 5 86 m s-1 in the winter) in the region
contributed decisively to the hydrodynamics (Lopardo
2002) and the formation of plankton and environmen-tal
gradients (Figs. 4, 5).
The Neusiedlersee in Austria and Hungary has
characteristics similar to those of Itapeva Lake
because its longitudinal axis is also more or less
parallel to the direction of the prevailing winds. The
water level in the largest bay of Neusiedlersee can
rise or fall 15 cm in a matter of hours, and the
phytoplankton composition differs substantially as
the wind direction changes. In this lake the most
important factor affecting phytoplankton composition
is the direction of the wind 1 day prior to sampling
(Padisa´k and Dokulil 1994). A similar time lag
between wind action and phytoplankton response was
found in another shallow lake (Millet and Cecchi
1992). The coupling between wind (velocity/direc-tion)
and community changes was observed at
Itapeva Lake in the autumn with a time lag of
approximately 24 h. The densities of Anabaena
circinalis and A. spiroides increased with a reduction
in the velocity of the SW wind over Itapeva Lake
(Becker et al. 2004). The duration of the wind events
and their associated hydrodynamics is a key factor for
spatial community changes.
Hydrodynamic variables, such as water level and
water velocity (not measured in the spring), induced
short-term spatial gradients. The environmental vari-ables
most strongly correlated with the seasonal
spatial gradient formation in Itapeva Lake were those
most directly influenced by with wind action
(namely, turbidity, suspended solids, and water
level). A suitable example of this occurred in the
summer, when the water dynamics driven by wind
(velocity and direction) generated a spatial–temporal
gradient (shifts within lake areas) in the structure of
the plankton community (Fig. 3). Spatial gradients
promoted by hydrodynamic variables could be seen
in the summer (March 1999), when the southern area
of the lake was more affected by winds from the NE,
and in the winter (August 1999), when the northern
area was most affected by a SW wind, both under a
long fetch.
When CCA ordinations of a different species
cluster are compared with the cluster of one only
species in particular, it is possible to evaluate the
potential target taxon for environmental monitoring
and conservation plans (Attayde and Bozelli 1998).
In Itapeva Lake, protists increase in density in situa-tions
of strong wind and long fetch (Cardoso and
Motta Marques 2004a). The opposite occurs with
cyanobacteria, which produce blooms in calmer sites
(Becker et al. 2004; Cardoso and Motta Marques
2004c) and in cold seasons (autumn and winter).
The choice of a sampling strategy has proven to be
the most important part of the methodology when the
aim of a study is to assess the spatial heterogeneity of
plankton (Lacroix and Lescher-Moutoue´ 1995; Pinel-
Alloul 1995; Pinel-Alloul et al. 1999; Thackeray et al.
123

11. Aquat Ecol (2009) 43:73–84 83
2004). Spatial heterogeneity is a common feature of
ecosystems and is the product of many interacting
biological and physical processes. However, there
have been few attempts to quantify the importance of
these physical effects by determining the proportion of
the spatial variance in plankton abundance that is
explained by broad-scale physical structuring of the
pelagic environment (Thackeray et al. 2004). In eco-logical
studies of zooplankton spatial heterogeneity,
sampling design must take into consideration the
pertinent spatial scales of physical and biological
variability because it is precisely these variables that
constitute the fundamental constraints to which indi-viduals,
populations, and communities respond (Pinel-
Alloul 1995). An understanding of the spatial struc-turing
of aquatic ecosystems would be furthered by
studies that adopt a quantitative approach to an
examination of the physical determination of the
spatial pattern over a series of survey dates (Thackeray
et al. 2004). The basic hypothesis that the wind drives
the spatial heterogeneity of plankton in Itapeva Lake
has been shown by Cardoso (2001), and other results
have been published by Cardoso and Motta Marques
(2003, 2004a,c). The present contribution reveals the
value of CCA as a tool to visualize spatial heteroge-neity,
and identify relevant determining variables.
Conclusion
Short-term patterns could be statistically demon-strated
using canonical correspondence analysis to
confirm the initial hypothesis. The link between
hydrodynamics and the plankton community in
Itapeva Lake was revealed using the appropriate
spatial and temporal sampling scales. As suggested
by our results, the central premise is that different
hydrodynamic processes and biological responses
may occur at different spatial and temporal scales.
A rapid plankton community response to wind-driven
hydrodynamics was recorded by the sampling
scheme used here, which took into account combi-nations
of spatial scales (horizontal) and time scale
(hours).
Acknowledgements We are grateful to the Brazilian agencies
FAPERGS (Fundac¸a˜o de Amparo a` Pesquisa no Rio Grande do
Sul) and CNPq (Conselho Nacional de Desenvolvimento
Cientı´fico e Tecnolo´gico) for grants in support of this research.
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