Journal of Plankton Research Vol.19 no.l pp.43-62, 1997
Spatial and temporal variations in the composition and density of
crustacean plankton in the five basins of Lake Kariba,
Zambia-Zimbabwe
Hillary M.Masundire1
University of Zimbabwe, Lake Kariba Research Station, PO Box 48, Kariba,
Zimbabwe
1
Present address: Department of Biological Sciences, University of Botswana, P.
Bag 0022, Gaborone, Botswana
Abstract Lake Kariba is a man-made reservoir which is now over 30 years old. The reservoir was built
on the Zambezi river on the border between Zambia and Zimbabwe. The crustacean zooplankton of
this man-made lake were studied over 3 years from March 1986 to February 1988. This period included
three seasons — (i) warm, rainy season, (ii) cool, dry season and (iii) warm, dry season — which have
a major influence on the limnology of the lake. Crustacean plankton species composition and abundance varied among the five basins. The most upstream basin had the highest number of species and
highest densities at all the sampling times over the period of this study. There was both spatial and temporal heterogeneity in species composition and abundance along the long axis of the lake.
Introduction
River-fed man-made reservoirs normally exhibit a spatial gradient in various
attributes such as sediment and nutrient concentrations. This, in turn, causes a
spatial gradient in biological productivity and water quality along the axis of the
reservoirs (Kimmel and Groeger, 1984, cited in Ryding and Rast, 1989). Lake
Kariba is -300 km long with five basins defined along the long axis of the lake
(Figure 1). The basins have varying degrees of individuality (Coche, 1974). The
depth of visibility (= Secchi-disc transparency) increases from Basin 1 to Basin 5
(Coche, 1974; Masundire, 1991). A slight reduction in transparency is observed in
Basin 5 (Coche, 1974; Masundire, 1989, 1991). The apparent colour of the lake
water also has some basin specificity (Coche, 1974).
In the first two basins, the flow of the Zambezi river may create strong water
currents such that at times (February-April) the whole of Basin 1 (Mlibizi basin)
and the upper half of Basin 2 (Binga basin) flow like a river. At such times, these
parts of the lake show riverine characteristics, such as strong currents and high
turbidities. Consequently, Basin 1 is influenced by the hydrology of the Zambezi
river from December to May and by local climatic factors for the rest of the year
when the flow of the Zambezi river is reduced (Coche, 1974). Basin 2 is influenced by both the hydrological regime of the Zambezi river and local climatic
factors. The other three basins are influenced by local climatic factors (Coche,
1974), although the hydrology of the Sanyati river strongly affects parts of Basin
5 (Sanyati basin).
A gradient in some physical and chemical parameters was observed among the
five basins. Over the period of this study, conductivity was -95 umS cm"1 in Mlibizi
and Binga basins, and -100 umS cm"1 in the other three basins (Magadza et al.,
© Oxford University Press
43
HMMasondire
f
X
0
50 ka
INSET
a
\
•
^
^
ZIMBABWE
<*»
X
^
ArM
X
©
•
Bjim
Sraplmj Station
_ _ Bain BouncUry
kittrnational border
Fig. L Map of Lake Kariba showing thefivebasins and sampling stations. The inset shows the position
of Lake Kariba.
unpublished). In the two upstream basins, alkalinity varied from 770 to 830 mEq
H, while in the three downstream basins alkalinity ranged from 880 to 910 mEq
H (Magadza et ai, unpublished).
No comprehensive quantitative studies on the zooplankton of Lake Kariba
covering the whole lake have been carried out since the lake was formed -30 years
ago. Preliminary results of this study (Masundire, 1989) were the first attempt at
a quantitative analysis of the zooplankton of all the five basins. Green (1985)
quantitatively sampled a large part of the lake from Basin 3 to Basin 5, but all the
samples in that study were collected on 1 day only. Other studies which analysed
samples from all five basins were only qualitative (Thomasson, 1965, 1980;
Korinek, 1984).
This paper discusses spatial and temporal variations in the crustacean plankton
populations in Lake Kariba.
Study area: Lake Kariba
Some of the major morphometric features of the lake are summarized in Table I.
These morphometric features vary with changes in lake level.
When compared with other African man-made lakes, Kariba is the third
largest in terms of surface area, after Lake Volta and Nasser-Nubia (Coche, 1974;
44
Variations in crustacean plankton in Lake Kariba
Table L Morphometry of Lake Kariba (from Coche, 1974)
Parameter (unit)
Symbol and formula
Length (km)
Mean breadth (km)
Surface area (km2)
Volume (106 m-1)
Mean depth (m)
Maximum depth (m)
Shoreline length (km)
Catchment area (km2)
Shoreline dev.
Volume dev.
Islands (number)
Islands surface area (km2)
Insulosiry (%)
/
b = Aoxr*
z = VoX Ao-<
2m
Lo
D L = L,, x 2OO1AJ-'
D v = 3z x z m -'
A,
A
Value
280.0
29.4
5500.0
160.5
29.2
120.0
2164.0
663820.0
8.3
2.1
293.0
146.9
2.7
Bernacsek, 1984). Coche (1974) defined five basins along the long axis of the lake.
Some features of the basins are described below. Place names and rivers are as
given on the Surveyor General map of Zimbabwe 1:1 000 000 of 1984.
Basin 1 (Mlibizi)
Mlibizi basin receives inflows from the Zambezi, Gwaai (via Zambezi), Mlibizi
and Sebungwe rivers. The Mlibizi and Sebungwe rivers are very seasonal in flow.
The Zambezi river has high flows from late December to April, during which time
the basin is very turbid. This basins holds -0.7% of the total lake's volume and
covers 1.7% of the lake's surface area (Table II).
Basin 2 (Binga)
Binga basin does not have any major tributaries draining into it. It receives inflows
from small seasonal streams. These include Lokola, Masumo and Senkwe on the
Zimbabwe shore, and Mwenda and Zhimu on the Zambian shore. From January
to March, part of this basin, from Sebungwe Narrows to Binga, is strongly influenced by currents created by the flow of the Zambezi. The basin holds -10.4% of
the lake's total volume and covers 12.5% of the lake's surface area (Table II).
Table IL Summary of basin morphometry
Parameter
Mlibizi
Binga
Sengwa
Bumi
Sanyati
Length (km)
Mean breadth (km)
Surface area (km2)
Mean depth (m)
Volume (106 m3)
23
4
91
13
1
56
12
677
24
16
%
21
2033
27
54
59
23
1386
33
46
46
27
1223
33
40
Basins 1,2 and 3 from Coche (1974) at 485 m a.s.1.
Basins 4 and 5 estimated from Surveyor General's map of Lake Kariba (1:100 000).
45
H-MJUasondire
Basin 3 (Sengwa)
The Sengwa basin receives inflows from Luizilukulu, Mwenda and Sengwa rivers
on the Zimbabwe shore, and from the Zongwe, Sikalamba, Nangombe, Cheziya
and Chibuwe on the Zambian shore. This is the largest of the five basins (Table II).
It holds -34.5% of the lake's volume and covers 37.6% of the lake's surface area.
Basin 4 (Bumi)
This basin receives inflows from the Sibilobilo and Ume rivers on the Zimbabwean shore, and the Lufua river on the Zambian shore. The basin holds -29.2%
of the lake's volume and covers -25.6% of the lake's surface area (Table II).
Basin 5 (Sanyati)
The Sanyati basin receives inflows from the Sanyati (Munyati), Gache Gache,
Nyaodza and Charara rivers. All these rivers are on the Zimbabwean shore. The
Sanyati river is the second largest river draining into Lake Kariba. The largest is
the Zambezi river. The basin holds -25.8% of the lake's volume and covers
-22.6% of the lake's surface area.
Method
Field sampling
Zooplankton were sampled from pelagic stations of Lake Kariba (Figure 1) using
a 2 m long 64 um plankton net with a mouth opening of 30 cm. The net was
lowered to as close to the bottom of the lake as possible and then hauled vertically at a uniform speed of -7 m min"1. The depth at each sampling station was
chosen using a Lowrance X-15M Computer Sonar echosounder fitted with a
Lowrance TTH 1192-20 transducer. This instrument often broke down due to
battery problems so that on many occasions the sampling depth was chosen intuitively. It was not always possible to sample from the bottom of the lake as the
many submerged trees were a major hazard to zooplankton nets. The samples
were concentrated into plastic bottles of 250 or 300 ml and immediately preserved
with -10% sugar-formalin (Haney and Hall, 1971).
Samples were collected from three stations in Mlibizi basin, four in Binga Basin,
four in Basin 3 (Sengwa basin), four in Basin 4 (Bumi basin) and four in Sanyati
basin (Figure 1). Sampling was carried out in March 1986, June 1986, August 1986,
March 1987, August 1987, October 1987 and February 1988. These sampling
periods correspond to (i) the warm, rainy season during which the lake was thermally stratified (February and March), (ii) the cool, dry season when the lake was
isothermal (June and August) and (iii) the warm, dry season during which the lake
thermally stratified (October).
Laboratory analyses
In the laboratory, the zooplankton samples were thoroughly mixed by shaking to
achieve a uniform distribution of the organisms. The density of organisms in the
46
Variations in crustacean plankton in Lake Karlba
sample bottles was visually assessed to determine whether or not to dilute the
sample(s) before subsampling. It was necessary to dilute most samples from
Mlibizi basin and a few from Binga basin. Samples from the other three basins
were never diluted.
Subsamples of 5 or 10 ml were placed in sedimentation chambers using a Socorex
adjustable-volume pipette whose tip had a 3 mm diameter opening. The volume of
subsample taken was determined subjectively according to the density of organisms
in the sample (Edmondson, 1971). In dense samples, small subsamples of 5 ml were
analysed, while in sparse samples, larger subsamples of 10 or 20 ml were analysed.
The subsamples were left to sediment for at least 1 h, after which they were analysed
on a Nikon Diaphot-TMD inverted microscope at 100 X magnification.
Species were identified from diagrams and descriptions in the literature (Sars,
1909; Kiefer, 1937; Sewell, 1957; Ward and Whipple, 1959; Einsle, 1971; RuttnerKolisko, 1974; Voigt and Koste, 1978; de Ridder, 1981; Korinek, 1984; Jeje and Fernando, 1986). All the crustacean plankton in the chamber were counted as
recommended in Edmondson and Winberg (1971), Edmondson (1974), Bottrell et
al. (1976) and Downing and Rigler (1984). At least 100 individuals of each species
or developmental stage were counted in order to achieve a coefficient of variation
of not more than 10% (Cassie, 1971). Where <100 specimens were encountered in
one subsample, either more subsamples were counted until there were at least 100
individuals or the whole sample was counted. This was commonly the case for all
stages of development of calanoids, Daphnia lumholtzi, Diaphanosoma excision and
Moina micrura. Bosmina longirostris, cyclopoid nauplii, copepodites and adults
were usually present in numbers well in excess of 100 specimens per subsample.
Calculation of densities
The density of the zooplankton species, groups or developmental stages was computed as follows:
Count of species/group/stage (i) in subsample = n
Volume of subsample = v
Volume of sample bottle = Vb
Number of organisms of species (j) in sample (A/,) = n • v 1 • Vb
Number of organisms of species (i) in lake sample = Nt
Density of species (i) in lake (ind. m~3) = A7,- • V^'1
where VL is the volume of lake water sampled. VL = wP-d, where r is the radius of
the mouth of the zooplankton net and d is the depth from which the net was vertically hauled up to the surface.
Multivariate analyses
Agglomerative hierarchical cluster analysis (Blackith and Reyment, 1971;
Norusis, 1985) was used to investigate whether basins and months in which
samples were collected formed homogeneous groups based on both the presence
47
H.M-Masnndire
and density of the zooplankton species. Basin means for the zooplankton variables were computed so that there was one value for each variable per basin.
These were then used as clustering variables to distinguish among the basins. Clusters were formed using squared Euclidean distance between counts standardized
to normal deviates. The significance of the separation of the basins or sampling
dates into clusters was tested using the binomial statistic. The statistic tested the
hypothesis that cluster separation was entirely a chance phenomenon. P is the
probability of getting exactly x successes in n independent binomial trials with the
probability of success on a single trial <t> equal to 0.5. Probabilities were read off
from CRC Standard Mathematical Tables (Selby, 1973). In cases where n > 20, a
normal distribution statistic was computed as: F{x) = [(x - $) - (n<£>)]/(n<£><$>)m
(Dixon and Massey, 1957). P was read as P = 1 - F{x) from the same tables.
Discriminant function analysis was used to investigate whether the five basins
were uniquely different from each other and to identify which, among the zooplankton, were the discriminating variables responsible for basin identity (Klecka,
1975; Norusis, 1985). Principal component analysis (PCA) was carried out in order
to identify any underlying relationships among the zooplankton variables (Kim,
1975; Norusis, 1985). PCA was used after testing the suitability of using factor
analysis on the data. The correlation matrix, anti-image correlation matrix,
Kaiser-Meyer-Olkin measure of sampling adequacy, Bartlet's test of sphericity
and its level of significance, all indicated that the data could be analysed using
factor analysis (Norusis, 1985).
These multivariate statistical analyses were carried out using procedures in the
SPSS* program (Norusis, 1985; SPSSx Inc., 1986). All computing was carried out
on a Data General MV 20000 computer. Twelve variables were used in the analyses: 1. BOSM = Bosmina longirostris; 2. DIAP = Diaphanosoma excisum; 3.
CRDP = Ceriodaphnia cornula;4. MOIN = Moina micrura; 5. DAPH = Daphnia
lumholtzi; 6. CYCN = cyclopoid naupli; 7. CYCC = cyclopoid copepodites; 8.
CYCM = cyclopoid male adults; 9. CYCF = cyclopoid female adults; 10. CALN =
calanoid nauplii; 11. CALC = calanoid copepodites; 12. CALA = calanoid adults.
The 19 sampling stations and/or six sampling dates were used as cases in cluster
and principal component analyses. The five basins were used as actual groups in
discriminant function analysis. The zooplankton densities were transformed to
natural logarithms prior to analysis (Cassie and Michael, 1968; Magadza, 1980).
Results
Zooplankton composition and densities
The crustacean plankton species found in the pelagial of thefivebasins are shown
in Table III.
Total zooplankton densities were always highest in Basin 1 (Mlibizi), followed
by Basin 2 (Binga) (Figure 2). Basins 3, 4 and 5 (Sengwa, Bumi and Sanyati)
always had lower densities in comparison to Basins 1 and 2; however, Basin 5
(Sanyati) usually had higher densities than Basins 3 and 4 (Sengwa and Bumi).
Plankton densities were highest in June or August, followed by October, and
lowest in February and March.
48
Variations in crustacean plankton in Lake Kariba
Table m. Pelagic crustacean plankton of Lake Kariba
Subclass: Branchiopoda
Order Cladocera
Alona sp. Baird 1850
Bosmina longirostris O. F. Muller 1785
Ceriodaphnia comuta Sars
Cdubia Sars
Chydorus sphacricus O. F. Muller
Daphnia lumholtzi Sars
Daphnia laevis Birge
Diaphanosoma excisum Sars
Moina micrura Kurz 1874
Subclass: Copepoda
Order: Cydopoda
Mesocyclops ooganus Onabamiro 1957
Macrocyclops albidus (Jurine)
Microcyclops sp.
Thermocyclops emini (Mrazek)
T.neglectus Sars 1901
T.hyalinus (Rehberg)
Order Calanoida
Thermodiaptomus syngemes (Keifer)
Tropodiapiomus kraepelini (Pope and Mrazek)
T. hutchinsonii (Keifer)
Order Harpacticoida
One unidentified species
Order Ostracoda
One unidentified species
Subclass: Malacostraca
Order Decapoda
Caridina sp.
The variations in the composition and density of the crustacean plankton with
time in each basin are illustrated in Figures 3-7, all drawn to the same scale.
Bosmina longirostris was usually the most abundant cladoceran in all five basins.
However, in Basin 1, Ccornuta or Diaphanosoma excisum were present in high
densities on some occasions (Figure 3). Daphnia lumholtzi was present only in
samples from Basin 1.
Among the cyclopoids, nauplii and copepodite stages were always more abundant
than the adult stages. Male cyclopoids were always less than female, except in Basin
1 in June 1986. In Basins 3,4 and 5, adult cyclopoids were always very few or even
absent (Figures 3-7). Calanoids were never abundant in all basins, except in Basin
1, in which densities of nearly 5 ind. H were sometimes recorded (Figures 3-7).
Figures 3-7 also compare the composition and density of crustacean plankton
at the same sampling times among the five basins. Basin 1 (Mlibizi) always had the
highest number of taxa and in greater densities than all other basins. This was followed by Basin 2 (Binga) and Basin 5 (Sanyati). Basins 3 and 4 (Sengwa and
Bumi) had the least number of taxa and generally the lowest densities. The
number of taxa recorded and their densities were always highest in June and
August, followed by October, and were lowest in February and March.
49
H-MJVIasnndlre
March 1986
June 1986
August 1986
March 1987
August 1987
October 1987
February 1988
Key
1 = Basin 1 (Mlibizi)
2 = Basin 2 (Binga)
3 = Basin 3 (Sengwa)
4 = Basin 4 (Bumi)
5 = Basin 5 (Sanyati)
Fig. 2. Total crustacean plankton densities among thefivebasins.
Multivariate analyses
Cluster analysis. Two large groups were formed after running cluster analysis.
When cluster cases were identified by the basins (Figure 8), one cluster was made
up of mostly Mlibizi and Binga basin samples, while the other was made up of
mostly Sengwa and Bumi basin samples. Sanyati basin samples were shared
between the two clusters. This analysis is summarized in Table III in which the percentage of samples of each basin belonging to one or the other cluster is shown.
When the cluster cases were identified by the month during which sampling was
carried out (Figure 9), one group put together February, March and October, while
SO
Variations in crustacean plankton in Lake Kariba
June 1986
August 1986
1 2 3 4 5 6 7 8 9 10 11 12
March 1987
August 1987
2 3 4 5 6 7 8 9 10 11 12
October 1987
1 2 3 4 5 6 7 8 9 1011 12
Key
1 » Bosmlna longirostris
21= Dlaphanosoma exdsum
3 = Certodaphrda comuta
4 = Daphnia lumhottzi
5 = Molna mlcnira
6 = cyctopokJ naupll
February 1988
1 2 3 4 5 6 7 8 9 1011 12
7 = cyctopold copepodrtes
8 o cyctopokl male adults
9 o cyclopold female adults
10 = calano)dnaupfl
11 = calartokj copepodltes
12 ° calanold adults
Fig. 3. Species composition and abundance of zooplankton in Basin 1 (Mlibizi).
the other put together June and August. This is summarized in Table V in which
the percentage of samples of each month belonging to one or the other cluster is
shown.
Using the binomial statistic (Table IV), Mlibizi and Bumi basins were shown to
be significantly different (P < 0.05) from the other three basins with reference to
species presence and abundance. Binga, Sengwa and Sanyati basins could be in
either of the two clusters. Samples collected in June and August, 1986, and those
collected in March and October, 1987, separated into two significantly different
clusters at a lower level of significance (P < 0.1) (Table V).
When basin means were used as clustering variables, Mlibizi basin was shown
to be unique from all the other basins (Figure 9). The other four basins formed
one cluster which further subdivided into two: one of which put together Basins 2
and 5, while the other put together Basins 3 and 4.
51
H.MJVlasundire
June 1986
1 2 3 4 5 6 7 8 9 10 11 12
March 1987
1 2 3 4 5 6 7 8 9 10 11 12
October 1987
1 2 3 4 5 6 7 8 9 10 11 12
Key
1 •= Bosmlna longlrostris
2 • Diaphanosoma excisum
3 •> Ceriodaphnit comma
4 = Daphnia lumholtzi
5 «• Moina mfcrura
6 = cyctopoW naupll
August 1986
1 2 3 4 5 6 7 8 9 10 11 12
August 1987
1 2 3 4 5 6 7 8 9 10 11 12
February 1988
1 2 3 4 5 6 7 8 9 10 11 12
7 = cyckipold copepodites
8 = cyclopoid male adults
9 - cyctopoW female adults
10 = calanoid naupll
11 = calanoid copepodites
12 = calanoid adults
Fig. 4. Species composition and abundance of zooplankton in Basin 2 (Binga).
Discriminant analysis. Discriminant analysis with the five basins as pre-determined groups derived four canonical discriminant functions (Table VI).
The eigenvector of the first canonical discriminant function is characterized by
Daphnia lumholtzi, calanoid adults, C.cornuta, calanoid nauplii and copepodites
(Table VI). This function has a canonical correlation coefficient R = 0.82 (Table
VI). The eigenvector of the second canonical discriminant function is characterized by cyclopoid female adults, cyclopoid male adults, Diaphanosoma excisum,
cyclopoid nauplii and copepodites, and B.longirostris (Table VII). This function
has a canonical correlation coefficient R = 0.58 (Table VI).
The first two canonical discriminant functions jointly account for 88.55% of the
total variance (Table VI). These are the discriminating functions. The third canonical discriminant function, accounting for 8.12% of the total variance, cannot be
52
Variations in crustacean plankton in Lake Kariba
June 1986
2 3 4 5 6 7 8 9 10 11 12
March 1987
August 1986
2 3 4 5 6 7 8 9 10 11 12
August 1987
2 3 4 5 6 7 8 9 10 11 12
October 1987
February 1988
2 3 4 5 6 7 8 9 10 11 12
Key
1 a Bosmlna longimstria
2 u Diapbanosoma axdsum
3 • Ceriodaphnia comma
4 e Daphnia lumhottzi
5 • Moina mlcrura
8 = cyctopold naupll
7 o cyctopoid copepodltes
8 = cyctopoid male adults
9 = cyctopokJ female adults
10-calanok) naupll
11 = calanoid copepodltes
12 »calanokj adults
Fig. 5. Species composition and abundance of zooplankton in Basin 3 (Sengwa).
identified by any specific variable. The eigenvector of the fourth canonical discriminant function, accounting for only 3.33% of the total variance (Table VI) is
characterized by M.micrum (Table VIII).
Wilk's lambda test (Table VI) showed that before any canonical discriminant
functions were derived, the group means were statistically different. The null
hypothesis (Ho) that there were no differences among the groups (basins), and that
the discriminant functions show only sampling variability, was therefore rejected.
After deriving the first two canonical discriminant functions, Wilk's lambda was
0.74,corresponding to a x2 of 25.58 with a significance level of P = 0.19. This meant
that the remaining variance was due more to within-group (intra-basin) than to
between-group (inter-basin) differences. The last two canonical discriminant functions cannot, therefore, sufficiently separate the groups (basins).
53
H.MJWasundire
June 1986
1 2 3 4 5 6 7 8 9 10 11 12
March 1987
1 2 3 4 5 6 7 8 9 10 11 12
October 1987
3 4 5 6 7 8 9 10 11 12
Key
1 o Bosmlna longirostrts
2 = Diaphanosoma exdsum
3 •= Cartodaptmta comuta
4 = Daphnla lumholtzi
5 o Molna micrum
6 " cycfopold naupd
August 1986
2 3 4 5 6 7 8 9 10 11 12
August 1987
2 3 4 5 6 7 8 9 10 11 12
February 1988
1 2 3 4 5 6 7 8 9 10 11 12
7 :-.cyctopotd copepodftes
8 •i cyctopotd male adults
9 •> cyctopoW female adults
10 = calanokj naupll
11 =catanold copepodltes
12 • calanold adulte
Fig. 6. Species composition and abundance of zooplankton in Basin 4 (Bumi).
The summary of the classification (Table VIII) shows that of all the samples that
were analysed, (i) 75% in Group 1 (Basin 1), (ii) 65% in Group 2 (Basin 2), (iii)
36.4% in Group 3 (Basin 3), (iv) 61.1% in Group 4 (Basin 4) and (v) 83.3% in
Group 5 (Basin 5) were correctly classified, i.e. they 'belonged' to the particular
pre-determined group (basin). This indicates that there was a relatively high degree
of individuality in Basins 1,2,4 and 5. Basin 3 had the least level of individuality.
The overall percentage of analysed samples that were correctly classified was
62.1%.
Principal component analysis. Three principal components were extracted,
accounting for 70.7% of the variance (Table IX). After varimax rotation of the
factors using Kaiser normalization (Norusis, 1985), three community types could
•54
Variations in crustacean plankton in Lake Kariba
June 1986
1 2 3 4 5 6 7 8 9 10 11 12
March 1987
1 2 3 4 5 6 7 8 9 10 11 12
October 1987
1 2 3 4 5 6 7 8 9 10 11 12
Key
1 - Bosmina longirostris
2 - Diaphanosoma excisum
3 = Ceriodaphnla comuta
4 •> Oaphnla lumhottzS
5 = Uolna mtcnira
6 - cyctopokJ naupli
August 1986
1 2 3 4 5 6 7 8 9 10 11 12
August 1987
1 2 3 4 5 6 7 8 9 10 11 12
February 1988
1 2 3 4 5 6 7 8 9 10 11 12
7 = cyclopoid copepodltes
8 = cyclopoid male adults
9 = cyclopoid female adults
10 = calanotd naupli
11 = calanoid copepodltes
12 = calanoW adults
Fig. 7. Speaes composition and abundance of zooplankton in Basin 5 (Sanyati).
be discerned by identifying those variables with high correlations with the three
principal components. These are marked with an asterisk in Table X.
Community 1 (PCA 1):
Bosmina longirostris, Diaphanosoma excisum, C. comuta, cyclopoid naupli, copepodites and adults
Community 2 (PCA 2):
Calanoid naupli, copepodites and adults.
Community 3 (PCA 3):
Daphnia lumholtzi and M.micrura.
55
HJV1.Mas un dire
10
15
20
25
Key
1 = Basin 1 (MlibizJ)
2 o Basin 2 (Blnga)
3 = Basin 3 (Sengwa)
4 = Basin 4 (Buml)
5 •= Basin 5 (Sanyatl)
i-
Fig. 8. Results of cluster analysis of the five basins
56
Variations in crustacean plankton in Lake Kariba
0
5
1
1
10
15
I
20
25
I
Dtnga
Sanyati —'
Sengwa
MBMzl
1
Fig. 9. Summary of cluster analysis of the basins.
Discussion
Variations in composition and density
Generally, zooplankton densities were highest in Mlibizi basin, followed by Binga
and then Sanyati basin, while densities were usually lowest in Sengwa and Bumi
basins. The plankton density gradient observed from Mlibizi to Bumi basins is
expected of river-fed man-made reservoirs because of a gradient in productivity
brought about by a spatial gradient in nutrient concentrations (Thornton et al.,
1982; Kimmel and Groeger, 1984; Rast et al., 1989).
Sanyati basin, the most downstream of the five basins, should be the least productive (Kimmel and Groeger, 1984), but it is not so because of the effect of high
nutrient inflows from the Sanyati river which empties into this basin (Magadza et
al., 1989). Sengwa and Ume rivers, which drain into Sengwa and Bumi basins,
respectively, do not have as much effect on pelagic zooplankton densities as does
the Sanyati river. This is probably because Sengwa and Ume rivers drain smaller
Table IV. Results of cluster analysis using basins as clustering variables
Cluster A
Mlibizi
Binga
Sengwa
Bumi
Sanyati
Ouster B
P
(binomial)
No.
%
No.
%
1
8
13
13
8
6.3
38.1
59.1
72.2
44.4
15
13
9
5
10
93.8
61.9
40.9
27.8
55.6
0.00"
0.19
0.26
0.03**
0.17
"P<0.05.
Table V. Results of cluster analysis using sampling dates as clustering variables
Ouster A
February 1988
March 1987
October 1987
June 1986
August 1986
August 1987
Cluster B
P
(binomial)
No.
%
No.
%
11
11
11
3
1
6
61.1
74.7
68.8
21.4
6.7
37.5
7
6
5
10
14
10
38.9
35.3
31.2
78.6
93.3
62.5
0.12
0.09*
0.06*
0.00"
0.00**
0.12
*P< 0.1; **P< 0.05.
57
H.MJVIasundire
Table VL Canonical discnminant functions
Function
Eigenvalue
% variance
Cumulative % variance
Canonical
corr. (R)
1
2
3
4
1.99
0.49
0.23
0.09
70.95
17.61
8.12
3.33
70.95
88.55
96.67
100.00
0.82
0.58
0.43
0.29
Table VIL Wilk's lambda test of significance of discriminant functions
After removing
function
Wilk's lambda
x2
Degrees of
freedom
P
0
1
2
3
0.17
0.5
0.75
0.91
153.3
59.6
25.2
7.7
48
33
20
9
0.00
0.00
0.19
0.57
catchments with lower levels of nutrient-generating human activities in comparison to the catchment of the Sanyati river.
There were very little differences in crustacean species composition among the
five basins. The most notable difference in composition was that of Daphnia
lumholtzi, the largest cladoceran in the lake. This was present only in Mlibizi basin.
It was not recorded in the pelagic zones of the other basins over the period of
study, although it was occasionally recorded from some river mouth locations in
Sanyati basin. Daphnia lumholtzi was recorded in the mouth of the Mwenda river
(Mills, 1977). The species may be present in all the basins, but only in special microhabitats such as river mouth areas. These areas tend to be richer in nutrients such
as nitrates and phosphates than pelagic sites.
One other factor that could account for the variation in crustacean plankton composition and density is water transparency. Mlibizi basin, where large zooplankters,
Table VIII. Eigenvector matrix of correlation between variables and canonical discriminant functions
D.lumholtzi
Calanoid adults
C comma
Calanoid naupli
Calanoid copepodites
Cyclopoid adult females
Cydopoid adult males
D.excisum
Cyclopoid naupli
Cyclopoid copepodites
B.bngirostris
M.micrura
1
2
3
4
0.61*
0.40*
038*
036*
036*
0.42
0.21
038
038
036
-0.04
0.14
-036
036
0.26
0.18
0.16
0.65*
0.47*
0.46*
0.45*
0.41
0.35*
0.26
0.16
0.25
-029
0.04
0.11
-0.08
-0.40
0.02
035
-0.01
0.18
-0.25
-0.06
-031
-0.01
-0.04
-0.22
-0.14
0.11
0.10
0.41
0.27
-0.04
0.4
•Variable with high correlation with a discriminant function.
58
Variations in crustacean plankton in Lake Kariba
Table DC Discriminant analysis, classification summary
Actual group
1 (Mlibizi)
2 (Binga)
3 (Sengwa)
4(Bumi)
5 (Sanyati)
No. of cases
16
21
22
18
18
Predicted group (basin) membership
1
2
3
4
5
75%
0
0
0
5.6
12.5
61.95%
22.7
5.6
0
0
9.5
36.4%
22.2
5.6
0
9.5
22.7
61.1%
5.6
12.5
19.0
18.2
11.1
833%
Daphnia lumholtzi and calanoid copepods were quantitatively important, always
had the lowest water transparency among the five basins (Masundire, 1991). These
zooplankters were present in the Sanyati basin (Masundire, 1994) during periods of
low water transparency. Water transparency may play a part in determining the
presence or absence and the abundance of these large zooplankters.
The occurrence of Daphnia lumholtzi and calanoid copepods may be influenced by predation on these large zooplankters by the pelagic Limnothrissa
miodon. Limnothrissa miodon is thought to be a visual predator (Begg, 1976;
Gliwicz, 1986; Masundire, 1989). Predation on large forms of zooplankton such as
Daphnia lumholtzi and calanoid copepods is expected to be reduced under conditions of low water transparency (Kerfoot, 1980). However, the relationship
between physical and biotic environmental factors on the one hand, and species
presence or absence on the other, may be a complex phenomenon. In Lake
Kariba, the periods of low transparency coincide with periods of high river inflows
which cause increased nutrient inputs (Masundire, 1991). These periods are, therefore, also associated with increased primary production due to the nutrient fluxes.
Consequently, these periods of low water transparency, as well as areas with low
water transparency, coincide with periods or places with high food availability.
There are no data on predator densities among the five basins, but data published by the Department of National Parks and Wildlife Management show that
the catch of L.miodon per unit effort varies by on order of magnitude among the
Table X. Rotated factor matrix (Varimax rotation) showing correlation between variables and principal component axes (*).
Variable
PCA1
PCA2
PCA3
BOSM
DIAP
CRDP
MOIN
DAPH
CYCN
CYCC
CYCM
CYCF
CALN
CALC
CALA
0.74*
0.63*
0.53*
030
-0.13
0.61*
0.74*
0.57*
0.75*
0.29
0.29
0.31
0.14
0.45
039
0.00
0.40
036
035
-0.02
039
0.90*
0.88*
0.88*
-0.05
0.14
0.46
0.69*
0.76*
0.14
029
0.51
0.25
0.16
0.11
0.13
59
H.M.Masundirc
basins (Sanyanga et al., 1990). This parameter can hardly be indicative of fish
(predator) densities as fishing effort is very variable among the basins. The gradient in zooplankton densities most likely reflects a gradient in productivity among
the basins.
Multivariate analyses
Multivariate analyses sought to discover the ecological uniqueness of the basins
with regard to crustacean plankton composition and densities. All three, cluster,
discriminant function and principal component analyses, showed that each basin
had some degree of individuality and that Mlibizi basin was uniquely different
from all the other four basins. The first discriminant function, which accounted for
-71% of variation among the basins, was characterized by the presence and abundance of rarer zooplankters, Daphnia lumholtzi, C.cornuta and all stages of
development of calanoid copepods. These are the real discriminating variables
among the basins.
Some of the differences among the basins can be explained with reference to
differences in hydrology and nutrient concentrations. The concentrations of
orthophosphate and nitrates were always highest in Mlibizi basin (Magadza et al.,
unpublished). Although the Zambezi river has been described as poor in nutrients (Coche, 1974; Marshall, 1982), it contributes up to 90% of water inflow into
Lake Kariba. It is, therefore, the major source of nutrients for the whole lake. The
hydrology of the Zambezi river is a major factor determining the nutrient status
of Lake Kariba, especially from Mlibizi to Bumi basins where there are no other
major inflows from tributary rivers.
The Sanyati river contributes -7% of annual water inflow into the lake
(Masundire, 1991). It brings in appreciable amounts of nutrients (Magadza et al.,
1987) which become distributed in the Sanyati basin. Thus, the nutrient status of
the Sanyati basin is greatly influenced by the hydrology of the Sanyatiriver.Nutrient concentrations in this basin tend to be higher than those in the middle basins,
Sengwa and Bumi. The Sanyati basin becomes similar to Binga basin with regard
to plankton biomass.
The community type 1, described by principal component analysis, is very
similar to that described by the same analysis by Magadza (1980) in the Sanyati
basin. However, C.cornuta, which formed part of this community type 1, was an
insignificant member of the zooplankton community described by Magadza
(1980). Calanoids formed the second community type, while the rare cladocerans,
Daphnia lumholtzi and M.micrura, formed the third crustacean community type.
Acknowledgements
This work was jointly sponsored by the University of Zimbabwe through
Research Grant RB/2.9999.10.2751 and the Swedish Agency for Research Cooperation with Developing Countries (SAREC). I am grateful to both for their
generous financial and material support. Most of this work was inspired by the
enthusiasm of and guidance from Prof. Chris Magadza, to whom I shall always be
60
Variations in crustacean plankton in Lake Kariba
grateful. I also wish to thank Prof. Jan Stenson of Goteborg's Universitet and Dr
Gunnar Andersson, formerly of the University of Lund, for all their academic and
social assistance during my travels to Sweden. This study was carried out at the
University of Zimbabwe's Lake Kariba Research Station, whose staff assisted me
variously, especially the crew of the research vessel, MV 'Erica'. I thank them most
sincerely.
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62