experimental assessment of nutrient enrichment and zooplankton … · 2016-01-11 · cyanophyta...

The Journal of Zoology Studies

Vol. 2 No. 5 2015 Journalofzoology.com

Page 6

The Journal of Zoology Studies 2015; 2(5): 06-17

ISSN 2348-5914

JOZS 2015; 2(5): 06-17

JOZS © 2015

Received: 23-12-2015

Accepted: 06-01-2016

Weldemariam Tesfahunegny

Ethiopian Biodiversity Institute,

Animal Biodiversity Directorate,

P.O. Box 30726, Addis Ababa,

Ethiopia

Corresponding Author:

Weldemariam Tesfahunegny

Ethiopian Biodiversity Institute,

Animal Biodiversity Directorate,

P.O. Box 30726, Addis Ababa,

Ethiopia

Experimental assessment of nutrient enrichment and

zooplankton predation effect on phytoplankton community

Author: Weldemariam Tesfahunegny

Abstract

Enclosure experiments were conducted over 18 days in December 2011 to assess the effects of

zooplankton predation and nutrient enrichment on phytoplankton community composition of

Gereb Beati reservoir (Tigray). Enclosures consisted of polyethylene tanks filled with 60 L of

filtered water. Experiments were carried out with removal and addition of nutrients cross-

classified with the absence and presence of zooplankton, resulting in an experimental design of

four treatment levels: (1) no nutrient addition, zooplankton absent, Z0N0 (Control); (2)

zooplankton absent, nutrient addition 260 g animal manure per 60 L of sample, Z0N1; (3) no

nutrient addition, zooplankton present, Z1N0; and (4) zooplankton present and nutrient added,

Z1N1. Four replicates were present for each treatment and samples were collected starting from

day 0 up to day 18 with three days interval, up to the end of the experiment. Phytoplankton

biomass expressed by chlorophyll a concentration showed 84.7% of the variation and that was

also positively associated with turbidity and pH; 7.9% of the variation observed in the second

axis was positively associated with zooplankton (D. cf. similis and Moina micrura) and it was

negatively associated with Microcystis (t= -4.207; p< 0.000). Seven zooplankton and 15

phytoplankton taxa were identified in the treatments. Nutrient addition (total phosphorus) was

more important explanatory variable than zooplankton predation in determining phytoplankton

abundance. D. cf. similis were able to suppress Microcystis in enclosures with low nutrient but

not in nutrient rich enclosures.

Keywords: Nutrient enrichment, Phytoplankton community, Tigray, Zooplankton predation

1. Introduction

In Tigray, there are more than 70 small reservoirs that have been created by micro dams that

attempt to provide water supply for irrigation and livestock drinking in the semi-arid highlands

of Northern Ethiopia, Tigray. The ages of those reservoirs varies between five and 20 years

(Nigussie et al. [23]

; Tsehaye et al. [31]

). Although, the reservoirs have considerable added value

to residents, their use as water resource was jeopardized by eutrophication and a high

occurrence of blooms of toxic cyanobacteria (Tadesse et al. [28]

).

Phytoplankton are composed of both eukaryotic and prokaryotic species. Harmful cyanobacteria

blooms are found among prokaryotic species, especially those of the toxic, non-nitrogen fixing,

colony forming cyanobacteria genus Microcystis, are symptomatic of advanced eutrophication

world-wide (Prepas and Charette [24]

; Wang et al. [33]

). Regardless of their world-wide

distribution, the mechanisms underlying the formation of surface algal blooms due to weak

predation by zooplankton or nutrient load in specific water bodies remain elusive and poorly

understood. Our inability to create experimentally controlled algal bloom conditions in the

laboratory is the greatest limitations to understand and predict phytoplankton bloom formation.



Page 7

Nutrient enriched aquatic ecosystem such as; lakes,

ponds, reservoirs and rivers resulted a frequent and

prolonged blooms of cyanobacteria display an array of

ecosystem properties that may have impacts on water

quality, biological communities and ecosystem services

(Crossetti and Bicudo [5]

). Phytoplankton biomass can

be regulated by abiotic and biotic factor such as

nutrients, light, herbivory, sedimentation and in the

case of reservoirs by large water volume losses (Mette

et al. [21]

). At the present time scientist, raise the

controversies whether zooplankton grazers, especially

large cladocerans like Daphnia, can effectively

diminish phytoplankton populations regardless of

nutrient conditions or the toxic of phytoplankton

resulted due to huge amount of nutrient fill controls

zooplankton abundance.

This study intends to look at the effects of zooplankton

predation and nutrient load from animal manures

flourishes on phytoplankton and Microcystis bloom

formation in Tigray reservoirs with enclosure

experimental assessment. This helps us to further

address the following questions: (i) Do zooplankton

predation or accumulation of animal manures play a

critical role in Microcystis bloom formation in Tigray

reservoirs (ii) To what extent is the effect Daphnia

predation on Microcystis bloom formation in nutrient

added and with no added nutrient treatments.

2. Methods

2.1 Study site and water sources

The enclosure experiment was performed in Mekelle

University laboratory of aquatic ecology. The water for

the bucket (enclosure) experiment was collected from

Gereb Beati reservoir. Gereb Beati is located 5 km

south away from the Mekelle University main compass

at 39º28'31'' N; 13º26'57" E in the semi-arid highlands

of Tigray. The surface area of the dam is 14 hr and

maximum depth is 9.5 m during wet season (Tsehaye

et al. [31]

). The experiment was started on December 10

and lasted on December 28, 2011.

The experiment was carried out in 16 enclosures of

polyethylene tank in the laboratory, by sampling water

from 10–28 December 2011. Zooplankton was

collected from Gereb Beati by making vertical hauls

with a conical zooplankton net (by collected water

using a 6-m vertical tow with 64 µm mesh), integrating

the entire water column of the reservoir. Experiment

was started by preparing nutrient medium, which was

prepared by drying and grinding of animal manure

after that sieved with sieve. The total N: P ratio was

measured (7.66:3.29) mg l- after homogenized the

entire prepared nutrients. The experiment was

conducted indoors by supplied florescent light in early

winter on December10, 2011 as day (0) and carry on

until December 28 as day (18) for duration of three

weeks. On the morning of December10, 60 L of pond

water (in16 enclosures) were collected and gently

mixed to ensure the even distribution of phytoplankton.

Half litter known Microcystis was added to the whole

enclosure. The containers were polyethylene tanks of

60 L (41 cm diameter and 59 cm height) each, which

were filled and placed on a cement platform in the

laboratory of aquatic ecology. There were three

treatments and a control, with four replicates each.

Details of the treatments have provided in the (Table

1).

Table 1: Experimental design of the treatment

Treatment Zooplankton in enclosures Animal dung

Z0N0 0 0

Z1N0 1 0

Z1N1 1 1

Z0N1 0 1

Note: (1) present and (0) absent; Z (zooplankton); N (nutrient).

After this, Z1 and Z0 represent mean presence and

absence of the initial naturally occurring zooplankton,

while N1 and N0 indicate either nutrient additions or no

nutrient additions, respectively. Nutrient was added

during the course of the experiment, at the start (day 0).

The amount of TN and TP present in the animal dung

was determined in ppm. An equal weight (260 g) of

cow manures was supplemented to eight enclosures

(Z1N1) and Z0N1) treatments with 4 replicate each at

day 0. In the nutrient addition treatments, TN (7.66

ppm) and TP (3.29 ppm) concentrations were

determined in comparison with the initial reservoir

water (1.29 ppm) and (1.79 ppm) respectively. Initial

phytoplankton of the enclosure is similar to the



Page 8

reservoir because the mesh size did not block the

iterance of phytoplankton to the plastic tank when

filtering to zooplankton with 64 µm mesh size plankton

nets. However, initially after collecting zooplankton

from reservoir with integrating sample we added half

litter of water with zooplankton to the treatment with

zooplankton only.

2.2 Sampling method

According to the technique Vanni and Temte [32]

as

much zooplankton (Z) and as little phytoplankton were

removed by using a 64µm nylon mesh. During the

experiment, the contents of the containers were gently

mixed in order to homogenize after the addition of

animal manure. The enclosures were sampled all the

biotic and abiotic variables at 3-days intervals during a

period of 18 days. We measured dissolved oxygen, pH

and temperature in situ with oxygen meter and water

transparency with Snell tube. Concentration of

chlorophyll a and turbidity were measured with a

fluorometer (Turner Aquafluor; average of three

measurements). All nutrient samples were kept in a

refrigerator box and frozen at-18 oC in the laboratory

of aquatic ecology until further analysis. The

concentrations of total phosphorus and total nitrogen

were measured following the ascorbic acid method and

the Kjeldahl method respectively (Anderson and

Ingram [2]

). Phytoplankton were counted and identified

to genus level following published manuals and

identification guides (Whitford and Schumacher [34]

;

Komarek and Anagnostidis [15, 16]

; John et al. [14]

).

Biomass in terms of carbon of cyanobacteria was

obtained through biovolume to biomass conversions

following counting (Hillebrand et al. [12]

; Menden-

Deuer and Lessard [20]

).

Zooplankton were sampled from two treatments (Z1N0

and Z1N1) by filtering one liters of water with a 64 µm

mesh and preserved with sucrose-saturated (4% final

concentration to prevent egg loss from cladocerans

carapaces) (Haney and Hall [10]

). Zooplanktons were

enumerated with stereomicroscope. A filtered sample

of known volumes (1 L) was taken from each treatment

(Z1N0and Z1N1); individuals of cladocerans and

copepods were counted and identified based on

published manuals and identification guides (Fernado [7]

). Cladocerans were determined to species level,

except for Diaphanosoma and Ceriodaphnia.

Copepods were determined to suborder level.

2.3 Data analysis

SPSS version 16, STTISTICA version 10 and

CANOCO version 4.5 were used to analyze the data.

Principal component analysis (PCA) plot was used to

show patterns of association among investigated

variables response to the presence and absence of

zooplankton and nutrient in experimental treatments.

Furthermore, we applied redundancy analysis (RDA;

Legendre and Legendre [18]

) to explain variation in a set

of variable response related to treatments effect such as

nutrient loading (TP, TN). All variables except pH

were log(x + 1) transformed prior to statistical analysis

to increase homogeneity of variances.

3. Results

3.1 Zooplankton and phytoplankton Species

composition During enclosure experimental assessment 7

zooplankton and 15 phytoplankton taxa were identified

in nutrient enriched and unenriched treatments which

are listed in (Table 2 and Table 3) respectively. Among

cladocerans D. cf. similis were highly dominated which

demonstrated a (Mean=25.64) (Table 2).while Moina

micrura revealed (Mean=1.44).

Table 2: Summary statistics of zooplankton count (indv. L-1

) in the enclosure during sampling day (experiment

assessment).

Zooplankton order Zooplankton taxa Mean Minimum Maximum

D.barbata 2.98 0 34

D.cf.similis 25.64 0 112

Diaphanosoma 15.04 0 81

Moina micrura 1.44 0 9

Ceriodaphnia 3.82 0 24

Cladocerans

48.87 0 181

Calanoid 9.9 0 78

Cyclopoid 7.73 0 38

Copepods

17.63 0 96



Page 9

Table 2: Phytoplankton species (pg C ml–1

) identified during experimental assessment in the enclosures.

Phylum Genes name Mean Minimum Maximum

Microcystis 2.05*105 0 2.3*10

6

Anabaena 178.49 0 6207

Psuedoanabaena 46.93 0 2606

Anabaenopsis 2336.34 0 2.2*106

Aphanizomenon 833.38 0 9.3*104

Cyanophyta

(Cyanobacteria) 2.13*10

5 0 23.5*10

5

Closterium 1813.69 0 1.4*104

Coenococcus 1.10*104 0 1.9*10

5

Cosmarium 461.31 0 9190

Pediastrum 6656.52 0 1.9*105

Scenedesmus 2323.50 0 3.7*104

Chlorophyta

(Green algae) 2.22*10

4 0 3.9*10

5

Aulacoseira 625.91 0 5823

Fragilaria 3928.78 0 2.2*104

Bacillarophyta

(Diatoms) 4554.69 0 2.2*10

4

Peridinium 2.23*105 0 1.3*10

6

Dinophyta

(Dinoflagellates) 2.23*10

5 0 1.3*10

6

Cryptomonas 1.60*104 0 1.4*10

5

Cryptophyta

(Cryptophyceae) 1.60*10

4 0 1.4*10

5

Trachelomonas 1.52*104 339 8.5*10

4

Euglinophyta

(Euglenophyceae) 1.52*10

4 339 8.5*10

4

Comparative high pH, water transparency and

dissolved oxygen were observed in treatments of Z1N0

and Z0N0. But, dissolved oxygen decreases in treatment

Z1N1 and Z0N1 from day15 onwards. Nutrient resulted

from the addition of cow dung in treatments Z1N1 and

Z0N1 have higher value comparing the other two

treatments.

The biomass of Cyanophyta and Euglinophyta

increases with nutrient addition. However, with the

presence of zooplankton the biomass of Cyanophyta

decreases (Table 5). Zooplankton presence had a strong

negative influence on Cyanophyta, with mean

Cyanophyta biomass an order of magnitude lower in

with zooplankton than without zooplankton enclosures

(t=-2.217; p<0.028) (Fig. 4). Furthermore, filamentous

cyanobacteria have (4.26 ± 0.372) of algal biomass in

with no zooplankton and nutrient enclosures but (4.72

± 0.273) without zooplankton and presence of nutrient.



Page 10

Nutrient addition had a much stronger influence on

algal biomass such as cyanobacteria, Dinophyta and

Euglinophyta community structure than did

zooplankton predation (Table 5). Increased

zooplankton grazing shifted to Cyanophyta and

Dinophyta dominance of the phytoplankton in

treatments.

Table 3: Summary statistics (Mean ± SE) of the measured environmental variables in the experiment treatments.

A biotic variables

Treatments

Z1N0

Mean ± SE

Z0N0

Mean ± SE

Z1N1

Mean ± SE

Z0N1

Mean ± SE

pH 7.88±0.025 7.91±0.02 7.60±0.02 7.58±0.02

Temperature (oC) 16.59±0.118 16.29±0.04 16.64±0.05 16.68±0.05

DO (mg/l) 6.20±0.123 6.57±0.09 2.11±0.13 2.20±0.09

Turbidity (NTU) 19.44±3.64 14.04±0.87 17.12±0.72 16.73±0.63

Transparency (cm) 30.21±1.71 36.32±1.68 18.67±1.17 16.82±0.94

TP (ppm) 0.034±0.01 0.02±0.01 2.46±0.12 2.50±0.02

TN (ppm) 3.25±0.23 3.13±0.45 4.22±0.60 3.12±0.36

NB: (NTU) Nephelometric turbidity unit, (ppm) parts per million.

Table 4: Average means (Mean ± SE) count of algal phylum (pg C ml–1

) contribution in four treatments during

experiment period.

Algal Phylum

Treatments

Z1N0

Mean ± SE

Z0N0

Mean ± SE

Z1N1

Mean ± SE

Z0N1

Mean ± SE

Bacillarophyta 3.1±0.157 3.22±0 .155 3.32±0.267 2.96±0.224

Chlorophyta 3.32±0.121 3.36±0.242 4.13±0.188 3.70±0.191

Cryptophyta 3.21±0.210 3.83±0.094 4.15±0.166 3.87±0.103

Cyanophyta 4.05±0.352 4.26±0.372 4.66±0.266 4.72±0.273

Dinophyta 4.05±0.277 5.08±0.157 4.15±0.365 4.20±0.340

Euglinophyta 3.77±0.113 3.80±0.082 4.27±0.061 3.99±0.087

Total 21.50±1.343 23.45±1.215 24.68±2.626 23.44±1.218

NB: Mean ± SE; value of mean, standard error in parenthesis of four treatments with their replicates along

experimental assessment (days).

3.2 Responses of total phytoplankton biomass to

nutrient

Total phosphorous has high positive peak value in the

treatments with nutrient addition (Table 6).

Phytoplankton biomass significantly increased with

nutrient addition (t= 3.15; p<0.002). High level of

nutrients (TP) is a major driving factor for

cyanobacteria abundance (Microcystis and Anabaena)

(t =2.87; p<0.005) (Table 6).



Page 11

Table 5: Results cross-checked with multiple regression analysis on effect of variables (Chlorophyll a, Microcystis,

D. cf. similis and D. barbata) in the experimental treatments.

6a. Chlorophyll a

Model: R² = 0.720; Adjusted R² = 0.692; F(10,101) = 25.94; p<0.000

Beta Std.Err. t(101) p-level

Intercept

2.36 0.020

Treatment 0.966 0.428 2.258 0.026

Time -0.575 0.075 -7.63 0.000

TP 1.187 0.376 3.155 0.002

D. cf. similis -0.493 0.181 -2.726 0.008

6b. Microcystis

Model: R²= 0.175 Adjusted R²=0.120; F (7, 104) =3.159; p < 0.004

Intercept Beta Std.Err. t(104) p-level

2.632 0.009

TP 1.616 0.562 2.874 0.005

D. cf. similis -0.671 0.301 -2.226 0.028

Treatment 1.668 0.643 2.595 0.011

6c. Daphnia cf. similis

Model: R²= 0.903; Adjusted R²= 0898; F (5,106) =197.20; p< 0.000


29.565 0.000

Treatment 2.146 0.073 29.472 0.000

Microcystis -0.070 0.032 -2.217 0.029

TP 0.1897 0.073 25.828 0.000

6d. Daphnia barbata

Model: R²= 0.639; Adjusted R²= 0.625; F (4,107) =47.263; p< 0.000


13.394 0.000

Treatment 1.870 0.140 13.392 0.000

Chl.a 0.167 0.062 2.707 0.008

TP 1.545 0.141 10.955 0.000

PCA axis 1 and 2 together represented 92.6% of

variation in the limnological variables of the enclosure

data set. Axis 1 (representing 84.7% of the variation

was mainly represented by phytoplankton biomass

(chlorophyll a concentration) and that was also

positively associated with turbidity and pH. This

gradient was negatively associated with time and water

temperature. Seven point nine percent of the variation

observed in axis 2 was positively represented by

zooplankton D. cf. similis and Moina micrura and it

was negatively associated with that of Microcystis. The

distribution of zooplankton Diaphanosoma,

Ceriodaphnia and Moina micrura negatively

associated with nutrients (Fig. 2).



Page 12

Fig 1: PCA ordination Biplot of a standardized PCA-analysis performed on the data of the experimental treatments.

PCA symbolize to (principal component analysis).

RDA: Results of all canonical axes test of significance

in four experimental treatments: Trace () = 0.072; F-

ratio = 2.776; P-value = 0.0010 (Fig. 3).

A redundancy analysis (RDA) of presence or absence

of phytoplankton (chlorophyll a, Anabaena and

Microcystis) revealed that nutrient addition contributes

more to the abundance of phytoplankton than to the

controlling effect of zooplankton predation (F = 2.776;

P = 0.0010) (Fig. 3).The first axis explains 35.2% of

the variation of phytoplankton, and the zooplanktons

D. cf. similis, Moina and Diaphanosoma were

positively associated with axis 1 and negatively

associated with presence of Microcystis. A redundancy

analysis (Fig. 3 found that variation in species

abundance among replicates was associated with the

zooplankton and nutrient addition treatments, as well

as their interaction. Zooplankton however have

revealed that a positive association with Anabaena.

Multiple regression analysis of the environmental

explanatory variables of chlorophyll a were treatment,

time, turbidity, total phosphors (F (10,101) = 25.94;

p<0.000) (Table 6a). Treatment postively affects the

abundance of phytoplankton abundance (chlorophyll

a), that is, in enclosures where there was limited or no

nutrient added, the biomass of phytoplankton remains

lower while in nutrient enriched enclosures the

phytoplankton biomass showed a significant increase

(Beta=1.616; p<0.000). Time and presence of D. cf.

similis negatively affect the concentration of

chlorophyll a (p<000 for time and p<0.008 for D. cf.

Similis, respectively). As time increases the

concentration of chlorphyll a was decreased. In

enclosures with higher zooplankton D. cf. similis, the

concentration of chlorphyll a showed a decrease in

magnitude.

The availability of total phosphorus positively explains

for the increased concentration of phytoplankton.

Similarly, the turbidity, which is also associated with

the addition of nutrients positively explained for the

abundance of phytoplankton (chlorophyll a

concentration). The influence of nutrient was more

(Beta=0.966) than that of the negative influence of D.

cf. similis (Beta=-0.493). Nutrient addition explains

more to the abundance of phytoplankton than being

controlled by D. cf. similis.

-1.5 1.0

-1.5

1.0

Time

pHTemperature

D.o

xyge

n

Chlrophyll a

Turbidity

Total nitrogen

Total phosphorus

D.barbat

D.c

f.sim

ils

Diapanosoma

Moina

Ceriodaphnia

Anabaena

Microcystis

Eigen value = 84.7%

Eig

en v

alue

= 7

.9%



Page 13

Fig 2: Summary of RDA plot outcomes, viewing the reaction of zooplankton predation and nutrient load in four

enclosure experimental treatments: Z0N0, Z1N0, Z1N1 and Z0N1. Keeping time as co-variable and zooplankton as

supplementary. Species are indicated by text, treatments are indicated by triangles and samples by empty circles.

An explanatory variables for Microcystis under

multiple regression analysis were total phosphors,

treatment with D. cf. similis (F (7, 104) =3.159;

p<0.004) (Table 6b). From this, treatment positively

affects for the abundance of Microcystis biomass (Beta

=1.668), however in the absence of nutrient

Microcystis negatively affected with D .cf. similis

(Beta =-0.671) (Table 6b and Fig. 4). Treatment effect

on biomass of Microcystis (Beta =1.668) is almost

similar to that of total phosphorous (Beta =1.616). The

biomass of Microcystis remains almost similar during

the experiment, while in the enclosures with D. cf.

similis, their biomass was significantly decreased at the

end of the experiment (see fig. 4).

Days

0 2 4 6 8 10 12 14 16 18 20

Mic

rocystis P

g C

- l

0

1

2

3

4

5

6

7

D.

cf.

sim

ilis

(in

/l)

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2.0

Microcystis

Microcystis no Daphnia

D. cf. similis

Fig 3: Comparison of performance of Microcystis in the presence and absence of D. cf. similis in treatments without

nutrient addition.

-0.6 1.2

-1.0

1.0

Chlorophyll aAnabaena

Microcystis+ Zooplankton

-No Zooplankton

+Zoop + Nutrient

-No Zoop + Nutrient

D. barbata

D.cf. similis

Diaphanosoma

Moina

Eigen value= 35.2%

Eig

en v

alue

= 24

.6%



Page 14

Results of multiple regression analysis of the biotic

explanatory variable for D. cf. similis were treatment,

Microcystis and total phosphors (F (5,106) =197.20;

p<0.000). D. cf. similis negatively affect the biomass of

Microcystis (Beta=-0.077) and treatments (Beta=-

2.146), but have positive impact with total

phosphorous (Beta =0.1897).

Multiple regression analysis of the environmental

explanatory variables of D. barbata were treatment,

chlorophyll a and total phosphors (F (4,107) = 47.263;

p<0.000) (Table 6d). Treatment negatively affects the

abundance of D. barbata abundance (Beta =1.870) in

treatment with the absence of nutrient D. barbata

density reduces comparing with nutrient Table 6d. In

enclosures where there is no nutrient added there were

less D. barbata while in nutrient added enclosures

there were more D. barbata and chlorophyll a

(Beta=0.167; p<0.000). In enclosures with higher

zooplankton, the concentration of chlorophyll a was

16.7% which affeted with grazing (Table 6).

The negative association of D. cf. similis with that of

Microcystis in relation to the control Microcystis

(enclosure without D. cf. similis) is demonstrated in

(Fig. 3) (t=-2.217; p<0.028). In enclosures with less D.

cf. similis there were high Microcystis but as D. cf.

similis abundance increases the biomass of Microcystis

decreases. However, in nutrient rich treatments effect

of zooplankton (D. cf. similis) were not able to control

Microcystis. Therefore, as zooplankton continued to

increase in densities, the Microcystis population started

to crash.

4. Discussion

Our data provide empirical support for a relationship

between limnological variables such as pH; average

temperature; dissolved oxygen and transparency. The

wide range of several limnological variables e.g.,

dissolved oxygen, pH, total nitrogen and total

phosphorus indicates that there were variations among

treatments in their physical and chemical properties.

This is in accordance to (Tsehaye [30]

) finding on

cyanobacteria occurrence showed well at high pH

because free CO2 concentrations are sufficient for

them. Overall, the differences in abiotic conditions

between enclosures with and without nutrient and

zooplankton in our study indicate that nutrient addition

can potentially have an impact on phytoplankton

community variations.

Enclosures with nutrients showed alkalinity properties.

This might be the alkaline nature of animal dung.

Besides the water bodies are alkaline by nature

(Tadesse et al. [28]

). Unlike the pH, dissolved oxygen

decrease in treatments with nutrient addition across

time, which might probably be due to algal

sedimentation. Water transparency is often taken as

indicator of phytoplankton biomass in the experimental

treatments, with the rationale that higher phytoplankton

concentration would lead to a decrease in water

transparency, which agrees to (Williams and Moss [35]

).

Our result demonstrated that availability of total

phosphorus and turbidity mainly explains for the

increase of phytoplankton abundance. Our findings are

in agreement with others, for example, Jensen et al. [13]

reported a direct relationship between total phosphors

concentration with abundance of both cyanobacteria

(Microcystis) and Chlorophyta. Besides, in our

experiment, total phosphorus concentrations cause

cyanobacteria to increase (Fig.5). This is in agreement

to (Tadesse et al. [28]

; Tsehaye [30]

) field survey which

revealed that most reservoirs in the eutrophic state

were heavily impacted by cyanobacterial blooms,

mainly Microcystis, with Chlorophyta and Dinophyta

as co-dominants. Phytoplankton community

composition to change with Cyanophyta and

Euglinophyta becoming the most dominant phyla with

treatment indicates that, algae vary in their competitive

abilities. Therefore, cyanobacteria might have a

competitive advantage in treatments, which are turbid

due to dense growths of other phytoplankton (Chorus

and Bartram [4]

). For example, Lange et al. [17]

showed

a variation between the response of benthic diatom

species to grazing and nutrient enrichment. This was in

disagreement with the finding of Wang et al. [33]

that

reported high concentrations of phytoplankton from

nutrient did not necessarily ensure cyanobacterial

blooms. Peridinium tended to dominate relatively in

the absence of nutrient treatments. This is similar to

Tsehaye [30]

report due to a lower nutrient in the deeper

reservoirs of Tigray might be less affected by

eutrophication.



Page 15

Days

0 2 4 6 8 10 12 14 16 18 20

Mic

rocystis (

PgC

/m)l

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

D.

cf.

sim

ilis (

ind/l)

1.2

1.4

1.6

1.8

2.0

2.2

Microcyst

Microcyst No Daphnia

D.cf.similis

Fig 4: Comparison of performance of Microcystis in nutrient rich treatment in relation to the presence and absence

of D. cf. similis.

D. cf. similis negatively affects the biomass of

Microcystis in treatment without nutrients. This might

be due to the inability of Microcystis colony size to

create mechanical interference for zooplankton grazing

in the absence of nutrient addition. This agrees to

Edmondson and Litt [6]

field observations, which

demonstrated that the presence of cyanobacterial

blooms usually related to the decreased abundance of

large-sized cladocerans and increased abundance of

rotifers, copepods and smaller sized cladocerans.

Similarly, there is evidence indicating that a

zooplankton population has adapted to tolerate

cyanobacteria (Sarnelle and Wilson [25]

) and some

species of aquatic grazers consume toxic cyanobacteria

without getting affected by it (Gremberghe et al. [8]

;

Agrawal and Agrawal [1]

; Boon et al., [3]

). This may be

the reason, why dense cyanobacterial blooms appear

only in few months of the year throughout the world.

In addition, the evolution of tolerance to poor-quality

and potentially toxic cyanobacteria by large species of

Daphnia may have important consequences for food-

web interactions and the response of lakes to

eutrophication (Hairston et al. [9]

). For example, such

adaptation might help to explain why large daphnids

are able to increase dramatically and strongly suppress

cyanobacterial biomass in absence of fish abundance.

This might be due to zooplankton are unconstrained by

predation in two trophic level systems. Recent

observations suggest that cladocerans populations may

also adapt to tolerate bloom-forming cyanobacteria in

their diets (Agrawal and Agrawal [1]

).

The increase of zooplankton density across

experimental treatment and time might be due to the

absence fishes (Wang et al. [33]

). In our finding, D. cf.

similis grazing was reducing phytoplankton species

with high variation with nutrient deficient treatments.

Zooplankton may regulate phytoplankton by grazing

(Sterner [27]

; Moegenburg and Vanni [22]

). However,

predation of copepods on larger species of

phytoplankton will favors colonial species of

cyanobacteria and green algae thus causing an increase

in their abundance, as observed in enclosure

experiments by (Sommer et al. [26]

).

The results of this study confirmed that nutrient

addition particularly phosphorus is an extremely

important factor that has a potential to influence the

structure of phytoplankton species (Microcystis bloom)

composition from enclosure experimental assessment.

These effects could change with the time duration and

season of the experimental assessment. Zooplankton,

especially D. cf. similis abundance increases in

treatments with nutrient addition across time. This

might be due to their capacity to produce hemoglobin,

which enables them to live in oxygen-reduced

environments (Lewis [19]

); this help them to achieve

high densities before Microcystis became dominant.

Tadesse et al. [29]

suggested a top-down effect of

Daphnia grazing on Microcystis in the reservoirs of

Tigray by an enclosure experiment, which is similar to

our result. Additionally, Gremberghe et al. [8]

reported

that since Microcystis did not produce strain of

microcystins, it is more reasonable that Daphnia

controlled growth of this strain by grazing. Besides,

calanoid copepods are reported to be adapted to graze

on large cyanobacteria (Haney [11]

).



Page 16

5. Conclusion

Our results in enclosure experimental assessment

provided an approach for examining zooplankton

predation on phytoplankton in nutrient enriched and

nutrient deficient condition. With this, we come up to

suggest three general conclusions. First, in treatments

with zooplankton and no nutrient addition, selective

grazing impact of zooplankton, D. cf. similis on

phytoplankton was observed. Secondly, the impact of

nutrient addition to phytoplankton blooming was much

greater than the negative impact of D. cf. similis.

Thirdly, phytoplankton not only influenced directly by

grazers, but highly on the supply of dissolved nutrient

availability. Additional experiment should be necessary

with the same method by measuring daily clearance

rate of zooplankton on dense Microcystis bloom

formation in the presence of both organic and inorganic

fertilizers during cold and warm conditions to study

specifically tolerant species of zooplankton species to

the toxic of cyanobacteria.

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