J. Agric. Environ. Sci. Vol. 7 No. 2 (2022) ISSN: 2616-3721 (Online); 2616-3713 (Print)

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 26

Association of some weather factors with fish assemblage in Asejire Lake, South-

western Nigeria

Mabel Omowumi Ipinmoroti

1

, Adams Ovie Iyiola*

1

, Olumuyiwa Ayodeji Akanmu

1

Department of Fisheries and Aquatic Resources Management, Faculty of Renewable Natural Resources

Management, College of Agriculture and Renewable Resources, Osun State University. P.M.B. 4494, Osogbo,

Osun State, Nigeria

*Corresponding author: adams.ovie.iyiola@gmail.com

Received: August 20, 2022 Accepted: November 5, 2022

Abstract: With the increasing human population, it is important to investigate the condition of Asejire Lake for

sustainability. To this end, the effects of some weather factors were investigated on the fish assemblage, so as to

provide necessary information to complement the dearth of reports about weather factors on the Lake. The study

area was partitioned into three stations (upper, middle and lower) with fortnight collection of water samples,

fish sampling and weather parameters for a period of 12 months (November 2017 – October 2018). Water

samples were measured in situ using appropriate kits for pH, ammonia, nitrite and nitrates, dissolved oxygen

and water temperature. Monofilament gill nets (40 mm and 60 mm) were used for fish sampling and were sorted

and identified using appropriate monographs. The mean values across the sampling stations for temperature,

pH, dissolved oxygen, ammonia, nitrates and nitrites were 18.36 ± 0.41

o

C, 7.30 ± 0.06, 2.37 ± 0.10 mg/L, 1.25 ±

0.05 mg/L, 1.34 ± 0.33 mg/L and 0.31 ± 0.03 mg/L, respectively. Across the months, mean values were 17.94 ±

0.48

o

C, 2.67 ± 0.21 mg/L, 7.22 ± 0.21, 0.23 ± 0.02 mg/L, 0.13 ± 0.02 mg/L and 3.03 ± 0.03 for temperature,

DO, pH, ammonia, nitrite and nitrates, respectively, with significant values (P < 0.05) among some parameters.

A total of 1443 individual fishes (720 in the dry and 723 in the wet season) belonging to 27 species were

encountered. March had the highest overall relative abundance of fish (23.77%) with Chrysichthys

nigrodigitatus being the most abundant species (39.32%). March (47.64%) and April (32.78%) recorded the

highest fish abundance in the dry and wet seasons respectively. Rainfall (540 mm) and temperature (35.50 °C)

were highest in the month of September. The trend of rainfall and temperature was observed to increase over

the months with t-values of 1.77 and 1.64 respectively. A negative relationship was observed between fish

abundance with temperature (b

1

= -1.08) and rainfall (b

1

= -0.26). It was observed that temperature values

increased and rainfall values varied. Therefore, activities must be geared towards environmental management

and consciousness of aquatic resources because of sustainability.

Keywords: Anthropogenic activities, Asejire Lake, Fish diversity, Water quality parameters

This work is licensed under a Creative Commons Attribution 4.0 International License

1. Introduction

Weather describes the day-to-day state of

atmospheric conditions of an area and it can be

influenced by interacting factors such as latitude,

elevation, nearby water, ocean currents,

topography, vegetation, prevailing winds and

human activities. When such conditions are above

or below recommended limits, it may alter the

physiological processes in the fish such as

spawning, survival, rate of recruitment into the

exploitable phase of the population, population

size, production, yield, food composition and

availability (Obot et al. 2016). Fish are connected

with their immediate environment and are therefore

highly vulnerable to changes in weather patterns.

These impacts can vary from the coastal areas to

the drier northern parts of the country (Froese et al.

2022). The effects of rainfall and temperature have

been reported to pose a significant impact on

Nigeria’s freshwater and marine aquatic systems

and hence on the country’s fisheries resources

(Gaines et al. 2018). The interplay of rainfall and

temperature governs other environmental factors

and they can predict the state of the atmosphere

(Sixth Assessment Report, 2021). The availability

of water in its right quality and quantity plays an

essential role in the existence of all living

organisms. This valued resource is increasingly

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Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 27

being threatened due to its use for various

economic, domestic and industrial uses by the

increasing human population (Froese et al. 2022).

Conversely, the fish species which make these

water bodies their haven are affected by water

pollution which may be due to the resultant

discharge of wastes directly or indirectly into the

water bodies. In such areas, fish may be affected in

terms of abundance, and biodiversity, migration

can occur when water quality is not tolerable and

death is imminent in extreme cases. Asejire Lake is

encompassed with various domestic and industrial

activities; a prominent one is the Nigerian Bottling

Company (NBC) plant which manufactures soft

carbonated drinks. The bottling plant extracts

portable water from the lake for manufacturing

activities and releases various solid and liquid

wastes into the environment. A constant discharge

of fumes was observed from the manufacturing

plants which releases carbon monoxide gases and

its derivatives into the atmosphere.

Another activity observed on the lake was the

intensive fish cage culture system by Triton

Company which releases all wastes from the fecal

and uneaten feed directly into the lake. Other

activities such as crop production, washing of

clothes by community inhabitants, water extraction

by tankers for domestic supply, dredging, bathing

and human defecation were also observed around

the lake. However, several studies on the effects of

various human activities on the water quality and

fisheries resources of the Lake have been reported

(Obot et al. 2016; Ipinmoroti et al. 2018), but there

is limited documented information on the effects of

weather patterns (rainfall and temperature) on the

fish biodiversity in the lake. It is pertinent to study

these at this time because of the current concerns of

global warming resulting from human activities

and the noticeable vulnerability of Nigeria to

climate change which has posed a major challenge

to fisheries (Omitoyin, 2009).

This study therefore proposes necessary

management procedures as elaborated by the

Agenda 2030 of the United Nations Sustainable

Development Goal number 14 (Life underwater).

These measures incorporate adaptation and

mitigation procedures towards climate resilience by

human activities as elaborated by SDG 13 (Climate

resilience). This study also seeks to investigate the

effects of rainfall and temperature on fish

assemblage in the Lake.

2. Materials and Methods

2.1. Description of the study area

Asejire Lake is a man-made lake that is created on

Osun River and is geographically located on

7.3669

o

N, 4.1333

o

E (Aladesanmi et al. 2013). It

was impounded in 1970 and supplies about 80

million liters of water per day to Asejire and

Osegere water treatment plants. About 80% of the

water is used for domestic purposes and the use of

chemicals around the lake is banned. Diverse

human activities such as agricultural activities,

laundry activities, and water withdrawals for

domestic uses were observed around the lake

catchment. Despite the ban on farming activities, it

was observed to be the predominant activity. The

lake has a mean depth of 11 m

2

, a length of 11.2

km, a surface area of 526 ha and a catchment area

of 7242 km

2

. The lake was partitioned based on

accessibility and logistical characteristics into three

sampling stations and sampled fortnightly from

November 2017 to October 2018.

Upper station (US): It was located about 300 m

away from the middle station and 750 m from the

dam wall. This station was located in the North

Eastern part of the Lake and characterized by

floating aquatic Macrophytes and a dense

population of vegetation around the catchment. A

few human activities such as washing and farming

were observed in this area.

Middle station (MS): It was located about 300 m

away from the upper station and 250 m away from

the lower station. This area was some wart in the

middle of the lake and human activities were

intense in this area. The Triton cage culture system

was located in this area, as well as increased

fishing activities because of the aggregation of fish

species around the cage area which feed on the

remains and escape of feed from the cages.

Lower station (LS): It was located towards the

Southern part of the Lake at 250 m away from the

middle station and 250 m away from the dam wall.

This area was close to the dam wall which received

all forms of waste flowing from the upper region of

the lake. Floating wastes such as plastics, nylon,

and floating aquatic Macrophytes were observed on

the water surface within this area. The spillway

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Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 28

which is used to regulate the lake water level was located in this area (Figure 1).

Figure 1: Map of Asejire Lake

2.2. Assessment of water quality

Water samples were collected fortnightly from each

station using 10 ml sterilized sampling bottles

between the hours of 0700 and 0900 GMT. The

samples were measured in situ for Dissolved

Oxygen (DO) concentration, temperature, pH,

ammonia, Nitrite and Nitrates from November

2017 – October 2018.

2.2.1. Dissolved oxygen

Dissolved oxygen (DO) was measured using a DO

meter manufactured by Lutron, United Kingdom

(Model DO-5509) as described by the

manufacturer. The meter was first calibrated and

the probe was inserted into about 10 cm of the

sample water so the water can cover the entire

sensor on the probe. Readings were taken on the

digital screen of the meter when it was steady and

recorded in milligrams per liter (mg/L). The probe

was rinsed after each measurement with tap water.

2.2.2. Water temperature

It was measured using a mercury-in-glass

thermometer which was dipped into the water

sample to a depth of 10 cm for about two minutes.

Readings were taken when the mercury level in the

thermometer was steady and recorded in degrees

Celsius (°C).

2.2.3. Ammonia

It was measured using API Freshwater Master Test

kit manufactured by MARS Fish care, United

States of America. Water samples were poured into

a 5 ml container and 2 drops of ammonia reagent A

were added to the sample. It was allowed to stand

for 30 seconds after which 1 drop of ammonia

reagent B was added to the mixture. It was later left

to stand for 10 seconds and the final colour of the

mixture was compared with the ammonia colour

chart provided by the manufacturer. Readings were

taken from the corresponding colour on the chart

and recorded in mg/L.

2.2.4. pH

The pH was measured using API Freshwater

Master Test kit manufactured by MARS Fish care,

United States of America. Water samples were

poured into a 5 ml container and 2 drops of the pH

reagent was added. The solution was left to stand

for 30 seconds and the final colour of the mixture

was compared with the colour chart so as to know

the corresponding pH value of the sample.

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Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 29

2.2.5. Nitrite

They were measured using API Freshwater Master

Test kit manufactured by MARS Fish care, United

States of America. Water sample were poured into

a 5 ml container and 2 drops of nitrate reagent was

added to the sample. The solution was left to stand

for 15 seconds and the final colour of the mixture

was compared with the colour chart provided by

the manufacturer. The corresponding value of the

final colour was read and recorded in mg/L.

2.2.6. Nitrates

They were measured using API Freshwater Master

Test kit manufactured by MARS Fish care, United

States of America. The sample water was poured

into a 5 ml container and 2 drops of nitrate reagent

was added to the sample. The mixture was left to

stand for 30 seconds and the final colour of the

mixture was compared with the nitrate colour chart

provided by the manufacturer. Readings were taken

and recorded in mg/L.

2.3. Assessment of fish abundance and

distribution

Fish species were collected fortnightly from the

sampling stations using monofilament gill nets of

mesh sizes 40 mm and 60 mm. These nets were set

at each sampling stations between the hours of

1900 GMT and retrieved at 0700 GMT the next

day as described by Kareem et al. (2015). The

gears were retrieved, fish species collected and

identified using monographs by Holden and Reeds

(1978); Olaosebikan and Raji (2013), and their

numerical abundance and distribution in each

station were recorded.

2.4. Weather parameters

2.4.1. Rainfall

It was measured fortnightly from November 2017 –

October 2018 using a standardized Stratus rain

gauge (Model 6330), manufactured in the United

States of America. It has a capacity of 280 mm, a

weight of 0.9/1.8 kg and a size of 102 mm x 356

mm. It was placed in an open area so as to prevent

obstruction from trees and ensure direct collection

of water from the atmosphere into the rain gauge.

The amount of rain collected was recorded in

millimeters (mm).

2.4.2. Atmospheric temperature

It was measured fortnightly from November 2017 –

October 2018 using Mason’s Hygrometer (wet and

dry bulb Thermometer) manufactured by Eisco

labs, United States of America. It is usually wall-

mounted and was placed around the Lake. It was

used to measure atmospheric temperature as

described by Camuffo (2019). The readings were

taken on the tube when the mercury level was

steady and values were recorded in degrees Celsius

(ºC).

2.5. Statistical analysis

Descriptive statistics, such as numeric counts,

percentages, means and standard deviations were

used on data for fish assemblages, water quality

parameters, and weather parameters. Turkeys’

pairwise comparison was used to determine the

level of significance among water quality

parameters across the months and sampling

stations. Linear regression analysis was used to

determine the association between fish abundance

and weather parameters (Equation 1). Linear trend

analysis was used to observe the trends of rainfall

and temperature over time (Equation 2). R-

Statistical software was used for all statistical

analysis at a 95% confidence level (P<0.05).

     [1]

       [2]

Where:

Y = Fish abundance

b

o

= Constant

b

1

= Regression coefficient/trend coefficient

X = Rainfall/temperature

Yt = Trends in rainfall/temperature

t = Time

3. Results and Discussion

3.1. Water quality parameters

The mean value of water quality parameters

measured from the sampling stations and across the

months during the study is presented in Tables 1

and 2 respectively. Across the sampling stations

and months, the mean values were highest in the

Middle station (18.44 ± 0.41 °C) and in February

(22.00 ± 0.68 °C). Most of the mean values

recorded from this study were below the

recommended limits of 20 – 30 ºC for aquatic biota

(FAO, 2022), except for February and April. The

temperature results deviated from the mean value

reports of 23.1°C and 25 °C from a Lake within the

region (Olanrewaju et al. 2017; Sunday and Jenyo-

Oni, 2018). Temperature is a very important

parameter because it regulates the internal

processes and body temperature of fish. Significant

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Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 30

differences (P<0.05) in mean values were observed

in December, July, August and September.

Most of the mean DO values across the sampling

stations and months were below the recommended

limits of 3 mg/L for aquatic biota (FAO, 2022),

with the highest mean values in the upper station

(2.71 ± 0.10 mg/L) and in June (3.64 ± 0.09 mg/L).

An overall mean value of 2.37 ± 0.10 mg/L was

measured across the sampling stations and it was

lower than the recommended limit for aquatic biota

(FAO, 2022). The DO concentration was observed

to be high at the onset of the wet season and low at

the end of the wet season. A possible reason could

be due to the low temperature and turbulence of

water by high winds. The DO level measured was

low in the dry season, which may be due to the

high metabolic rate and limited water turbulence.

The low mean values were expected because of the

increased levels of ammonia which inhibited DO in

the lake thereby affecting fish species distribution

(Beggel et al. 2021). The mean DO values in May

– September were observed to be slightly above the

recommended limits with significant monthly

differences (P<0.05) observed in February, April,

June, August and September. Across the sampling

stations, the mean value in the middle section was

observed to be significantly different (P<0.05) from

other sections during the study. A possible reason

for this may be the presence of the cage culture

system which involves the intensification of

supplementary feed and increased waste generation

(Beggel et al. 2021).

Across the sampling months and stations, all the

pH values were above the recommended limits of

6.5 – 8 for aquatic biota (FAO, 2022). Across the

sampling stations, the highest pH was measured in

the upper station (7.32 ± 0.06) with an overall

mean value of 7.30 ± 0.06. For the sampling

months, February had the highest mean monthly

value (7.8 ± 0.00), and an overall mean monthly

value was 7.29 ± 0.21. Significant monthly

differences (P < 0.05) in mean pH values were

observed in January, February, March, June,

August and September. The pH value recorded

from this study suggested that the condition of the

water is between neutral to a slightly alkaline

condition and is a tolerable level for the survival of

fish species (Farombi et al. 2014; Obot et al. 2016).

Ammonia, nitrite and nitrates are products of

decomposition. Nitrites are produced from a

combination of Nitrosomonas bacteria and nitrates

by Nitrobacter bacteria. The ammonia values

across the sampling stations and months were

extremely high when compared with recommended

levels for aquatic biota (FAO, 2022). The highest

mean value across the sampling stations was

recorded at the middle station (1.96 ± 0.05 mg/L),

and an overall mean value of 1.96 ± 0.05 mg/L was

measured during the entire study. This was

expected because the location of the cage culture

system was in this area and it releases huge wastes

from uneaten feed, excretory products and organic

decomposition from the intensive culture system

carried out (Beggel et al. 2021; Makori et al. 2017).

This activity influenced the low dissolved oxygen

as observed from the mean values in this station

(Beggel et al. 2021).

Across the sampling months, the mean

concentration of ammonia was highest in August

(0.50 ± 0.03 mg/L) and September (0.50 ± 0.00

mg/L), and an overall mean of 0.21 ± 0.02 mg/L

was measured during the entire study. These values

were also higher than the recommended limit of

0.05 mg/L for aquatic biota (FAO, 2022), and

significant monthly differences (P<0.05) were

observed in the mean values measured in May and

June. Elevated ammonia levels are not tolerable to

fish because it can cause gill damage and inhibit

DO, therefore its levels should be minimized

(Sunday and Jenyo-Oni, 2018). Significant

differences (P<0.05) were observed in mean

ammonia levels measured in the middle and lower

stations.

Table 1: Mean water quality parameters measured across the sampling stations

Parameters

Upper Station

Middle Station

Lower Station

Mean values

Recommended

(FAO, 2022)

Temperature (

°

C)

18.30 ± 0.39

18.44 ± 0.41

18.33 ± 0.42

18.36 ± 0.41

20 – 30

DO (mg/L)

2.71 ± 0.10

b

2.00 ± 0.10

a

2.41 ± 0.10

b

2.37 ± 0.10

3

pH

7.32 ± 0.06

7.29 ± 0.05

7.30 ± 0.06

6.5 – 8

Ammonia (mg/L)

0.26 ± 0.06

b

1.96 ± 0.05

a

1.53 ± 0.05

a

1.25 ± 0.05

0.05

Nitrite (mg/L)

0.07 ± 0.03

b

0.67 ± 0.03

a

0.19 ± 0.03

b

0.31 ± 0.03

0.25

Nitrate (mg/L)

0.02 ± 0.33

b

2.27 ± 0.34

a

1.72 ± 0.32

b

1.34 ± 0.33

250

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Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 31

± is the Standard Error of Mean (SEM); mean values with different superscripts are significantly different across

the rows

Nitrite which occurs from the breakdown of

ammonia was high in the middle station with a

mean value of 0.67 ± 0.03 mg/L and an overall

mean total of 0.31 ± 0.03 mg/L (Table 1). The

mean values measured in the middle station were

the only value above the recommended limit of

0.25 mg/L for aquatic biota (FAO, 2022). Across

the sampling months, July had the highest mean

value with 0.25 ± 0.09 mg/L and an overall mean

of 0.12 ± 0.02 mg/L. Nitrite values were not

detected in January, February and March with

recorded mean monthly values (0.13 ± 0.02 mg/L)

within the recommended limit of 0.25 mg/L (Table

2). The results from this study deviated from the

reported mean values of 0.21 mg/L for aquatic

biota (FAO, 2022) and 0.23 mg/L (Farombi et al.

2014). Nitrates are less toxic and mean values

measured across the months (3.03 ± 0.03 mg/L)

and sampling stations (1.34 ± 0.33 mg/L) were

within the recommended levels of 250mg/L for

aquatic biota (FAO, 2022), with similar values

reported by Farombi et al. (2014).

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Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 32

Table 2: Mean water quality parameters measured across the sampling months

Season

Months

Temperature (°C)

DO (mg/L)

pH

Ammonia (mg/L)

Nitrite (mg/L)

Nitrate (mg/L)

Dry

November

18.30 ± 0.26

b

2.80 ± 0.04

b

7.10 ± 0.01

b

0.13 ± 0.01

b

0.19 ± 0.02

b

5.29 ± 0.00

a

December

17.30 ± 0.33

a

2.91 ± 0.04

a

7.00 ± 0.02

b

0.33 ± 0.00

b

0.21 ± 0.00

b

5.02 ± 0.00

a

January

19.67 ± 0.21

b

2.00 ± 0.00

b

7.73 ± 0.04

a

ND

February

22.00 ± 0.68

b

1.77 ± 0.00

a

7.8 ± 0.00

a

ND

March

19.68 ± 0.33

b

2.00 ± 0.05

b

7.73 ± 0.04

a

ND

Wet

April

20.0 ± 0.26

b

2.1 ± 0.04

a

7.2 ± 0.06

b

0.25 ± 0.00

b

0.20 ± 0.04

b

ND

May

18.00 ± 0.51

b

3.5 ± 0.16

b

7.10 ± 0.02

0.08 ± 0.05

a

ND

June

18.00 ± 0.26

b

3.64 ± 0.09

a

7.27 ± 0.04

a

0.13 ± 0.08

a

0.17 ± 0.05

a

5.67 ± 1.05

a

July

14.33 ± 0.21

a

3.23 ± 0.11

b

7.00 ± 0.00

b

0.43 ± 0.05

b

0.25 ± 0.09

b

5.20 ± 0.00

a

August

15.00 ± 0.00

a

3.00 ± 0.00

a

6.90 ± 0.00

a

0.50 ± 0.03

b

0.22 ± 0.01

b

5.00 ± 0.00

a

September

15.00 ± 0.00

a

3.00 ± 0.00

a

6.90 ± 0.03

a

0.50 ± 0.00

b

0.22 ± 0.03

b

5.00 ± 0.00

a

October

18.00 ± 0.51

b

2.10 ± 0.04

b

7.00 ± 0.03

b

0.13 ± 0.02

b

0.20 ± 0.01

b

5.02 ± 0.00

a

Mean Total

17.94 ± 0.48

2.67 ± 0.21

7.22 ± 0.21

0.23 ± 0.02

0.13 ± 0.02

3.03 ± 0.03

± is the Standard Error of Mean (SEM); ND – Not Detected; values with different superscripts across each column are significantly different (P<0.05)

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Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 33

3.2. Assessment of fish abundance and

distribution

A total of 1443 individuals belonging to 27 species

were identified with the highest abundance and

distribution in the lower section (36.04%) which

was close to the dam wall (Table 3). This was

expected because the dam wall had created an

obstruction which allowed aggregates of food

materials and the abundance of fish species was

eminent. The cage culture system was located in

the middle section of the Lake and it recorded fish

abundance (32.08%) which was close to the

abundance encountered in the lower station. This

was expected because fish species will aggregate

around the cages to consume uneaten feed which

finds its way out of the cages.

Despite the huge ammonia load (1.96 ± 0.05

mg/L), fish were abundant and must have devised

means of survival in the middle section. Ipinmoroti

et al. (2018) studied the abundance of fish species

in the lake and reported an abundance of 1780

belonging to 19 species, which was higher than the

abundance during this study but fewer species.

Similarly, 27 species were reported by Ipinmoroti

(2013) in the Lake. In the dry season (November

2017 – March 2018), a total of 720 individuals

were encountered with March recording the most

abundant fish species (47.64%). In the wet season

(April – October 2022), a total of 723 individuals

were encountered, with April recording the most

fish species abundance (32.78%). It was observed

that these two periods mark the transition between

the dry and wet seasons and the natural instincts of

fish species are expectant for a change in condition

during this period (Negi and Mangin, 2013).

Table 3: Relative abundance and monthly fish distribution in the stations

Seasons

Months

US

MS

LS

Total

Total (%)

T/Sn

T/Sn(%)

Dry

November

11

14

11

67

4.64

720

9.31

December

9

10

9

68

4.71

9.44

January

66

44

66

145

10.05

20.14

February

40

34

40

97

6.72

13.47

March

92

144

92

343

23.77

47.64

Wet

April

76

85

76

237

16.42

723

32.78

May

24

39

24

79

5.47

10.93

June

105

47

105

234

16.22

32.37

July

16

11

16

44

3.05

6.09

August

4

6

4

36

2.49

4.98

September

8

18

8

35

2.43

4.84

October

9

11

9

58

4.02

8.02

Total

460

463

520

1443

100

1443

Total (%)

31.87

32.08

36.04

100

US – Upper section, MS – middle section, LS- lower section, T/Sn – total per season, T/Sn (%) – relative

abundance of fish per month season

Across the sampling months, March had the

highest relative abundance (23.70%) with

Chrysichthys nigrodigitatus the most abundant

(39.32%) fish species (Table 4). The dominance of

C. nigrodigitatus have been reported in Owalla and

Eko-Ende reservoirs (Taiwo, 2010) and Aiba

Reservoir (Iyiola et al. 2019) which are located

within the Osun river system. In contrast,

Ipinmoroti et al. (2018) reported the dominance of

Tilapia marie in the Lake. The fish abundance

fluctuated over the months with the abundance

higher in the dry season (47.20%). This was

expected because breeding activities for most fish

species had ceased due to reduced rainfall and

limited food availability, therefore fish species will

aggregate in open waters (Negi and Mangin, 2013).

The total fish abundance recorded from the Lake

during the study was low (1443) when compared to

the reported results of 1780 individuals comprising

19 species (Ipinmoroti et al. 2018), and was lower

than the number of species identified in this study.

J. Agric. Environ. Sci. Vol. 7 No. 2 (2022) ISSN: 2616-3721 (Online); 2616-3713 (Print)

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 34

Table 4: Relative abundance of fish species across the months

S/N

Fish species

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sept

Oct

Nov

Dec

Total

Total (%)

1

Alestes baremoze

0

8

57

3

10

0

3

0

84

5.81

2

Alestes dentex

12

0

2

0

14

0.97

3

Brycinus longipinis

0

3

21

0

24

1.66

4

Chrysichthys nigroditatus

39

43

213

63

18

125

38

0

4

5

10

11

569

39.32

5

Citharinus citharus

0

1

0

5

2

9

17

1.17

6

Clarias gariepinus

0

1

0

1

0

2

0.14

7

Clarias macromystax

0

1

0

3

0.21

8

Coptodon mariae

0

2

15

7

0

11

8

18

61

4.22

9

Coptodon zilli

0

86

0

32

0

12

2

132

9.12

10

Cromeria occidentalis

0

13

0

5

4

0

22

1.52

11

Distichodus rostratus

11

5

0

2

0

2

4

0

24

1.66

12

Hepsetus odoe

0

1

0

2

0

7

10

0.69

13

Hemichromis fasciatus

0

1

0

2

3

0.21

14

Hydrocynus forskahlii

18

0

5

1

8

7

0

1

0

40

2.76

15

Hyperopisus bebe

0

1

0

3

0

2

6

0.41

16

Lates niloticus

0

1

0

1

0

5

0.35

17

Mormyrups aguilloides

0

2

0

3

4

9

2

1

21

1.45

18

Mormyrus rume rume

14

6

0

8

0

34

2.35

19

Oreochromis aureus

0

2

3

0

1

2

3

1

12

0.83

20

Oreochromis niloticus

13

3

5

2

22

8

1

11

0

7

8

0

80

5.53

21

Parachanna obscura

7

6

0

2

15

1.04

22

Polypterus senegalensis

0

1

0

1

2

0.14

23

Synodontis batensoda

0

1

0

1

0

11

0

8

2

1

24

1.66

24

Sarotherondon galilaeus

0

2

0

1

0

10

0

4

0

17

1.17

25

Synodontis marophthalamus

0

1

0

2

0

8

11

0.76

26

Schilbe mystus

23

4

23

63

4

11

1

7

2

7

4

151

10.44

27

Synodontis budgetti

0

5

9

11

7

9

3

0

8

0

5

3

60

4.15

Total

137

94

342

236

79

234

44

36

25

68

76

72

1443

Total (%)

9.49

6.51

23.70

16.35

5.47

16.22

3.05

2.49

1.73

4.71

5.54

4.71

J. Agric. Environ. Sci. Vol. 7 No. 2 (2022) ISSN: 2616-3721 (Online); 2616-3713 (Print)

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 35

3.3. Weather distribution

3.3.1. Weather condition parameters

The mean rainfall and atmospheric temperature

measured during the study are presented in Table 5.

The wet season is often characterized by high rains

and reduced temperature while the dry season is

characterized by low rains and elevated

temperature regimes (NiMET, 2019). For both

parameters, a fluctuating trend was observed with

mean values scattered along the line of trend fit.

Total rainfall of 2940 mm was recorded throughout

the study, with the highest in September (540 mm)

and the least in January (70 mm). The highest and

least mean rainfall values recorded from this study

were as expected and corroborated by reports of

NiMET (2019).

With relation to fish abundance, September which

had the least fish abundance (2.0%) recorded the

highest value for mean rainfall (540 mm) and

atmospheric temperature (35.50 °C) during the

study. The possible reason for this may be the

response of fish species to increased rainfall and

breeding activities in which they migrate to shallow

regions for breeding activities (Negi and Mangin,

2013).

Conversely, December which is the peak of the dry

season recorded the least rainfall (45 mm) and was

expected to record the highest abundance of fish

species (Table 5). However, December recorded a

relative small abundance of 4.71% and the highest

abundance was recorded in March (23.77 %),

which denotes the end of the dry season. This

occurrence may be due to the expectance of the

rains by fish species in April which is the onset of

wet seasons breeding activities may commence.

Negi and Mangin (2013) reported similar

occurrences in Tons River, India. The results on

mean temperature from this study deviated from

the statements of NiMET (2019) because the wet

season which is supposed to be characterized by

low temperatures had the highest monthly

temperatures (Table 5) and the dry season had the

least monthly temperatures instead of measuring

the highest monthly temperature ranges. This

clearly expresses a change in weather pattern from

the normal deviation in characteristics of wet and

dry seasons (Omitoyin, 2009; NiMET, 2019; Sixth

Assessment Report, 2021).

Table 5: Mean rainfall and temperature and total fish abundance

Month

Mean Rainfall (mm)

Atmospheric Temperature (

°

C)

Total fish abundance

November

87

34.10

67

December

45

32.11

68

January

70

20.20

145

February

110

24.50

97

March

150

25.50

343

April

170

29.00

237

May

510

29.00

79

June

430

28.00

234

July

360

32.00

44

August

500

33.00

36

September

540

35.50

35

October

218

33.20

58

Total

3190

29.68

1443

3.3.2. Linear trend analysis

The linear trend analysis of rainfall and

atmospheric temperature over time is presented in

Table 6. Statistically, a positive trend that

fluctuated across the months was observed over

time. This indicated that the mean rainfall (t = 1.77)

and average atmospheric temperature (t = 1.65)

presented an increasing trend over the months. It

explains that for a unit increase in time (months),

mean rainfall has increased by 1.77 units and the

average atmospheric temperature has increased by

1.645 units. Therefore, it may be said that the

weather parameters measured during the study has

increased over the months of the study.

3.4. Association between weather conditions

and fish abundance

The association between fish abundance and

weather factors is presented in Table 7.

Statistically, the relationship was observed between

fish abundance and weather parameters was not

significant (P>0.05), but negative. The negative

value implies that as one increases, the other

J. Agric. Environ. Sci. Vol. 7 No. 2 (2022) ISSN: 2616-3721 (Online); 2616-3713 (Print)

Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 36

decreases; as rainfall (b

1

= -0.27) and temperature

(b

1

= -1.08) increased by one unit, fish abundance

reduced by 0.27 units and 1.08 units respectively. It

may therefore be said that the observed increase in

temperature (Table 6) has declined the fish

abundance over the time of study thereby posing

negatively on the sustainability of fish species in

the reservoir. With these effects, the supply of fish

to the rural and urban populace is affected and

measures to promote sustainability through

management procedures are essential.

Table 6: Linear trend analysis for rainfall and temperature over time

Parameters

b

o

b

1

Relationship

Model Y

t

=b

o

+ b

1

*t

Rainfall (mm)

32.50

1.77

Positive

Yt = 3.25 + 1.77*t

Temperature (ºC)

20.30

1.65

Positive

Yt = 20.30 + 1.65*t

Yt = Rainfall/Temperature, b

o

= constant, b

1

= trend coefficient, t = time

Table 7: Regression coefficients between weather factors and fish abundance

Parameters

b

o

b

1

P-value

Relationship

Model Y

t

=b

o

+ b

1

X

Rainfall (mm)

224.3

-0.27

0.24

a

Negative

Y = 224.3 – 0.27 X

Temperature (°C)

480.4

-1.08

0.16

a

Negative

Y = 480.4 – 1.08X

Y = Fish abundance; b

o

= constant, b

1

= regression coefficient, X = Mean rainfall/ average atmospheric

temperature; P-value with different superscript are significant at P<0.05.

4. Conclusions

It was observed that the Lake was affected by the

prevailing water conditions indicated by the low

dissolved oxygen and high ammonia and nitrite

concentrations. This affected the fish species

distribution and abundance in the Lake.

Temperature values were observed to have

increased across the months while mean rainfall

values were variable. These patterns were observed

to have a negative effect on the fish species by

reducing their abundance, and if it persists their

sustainability in the lake is questionable. To

address this issue, measures must be in place to

ensure a healthy environment in terms of waste

discharge into the Lake and gases to the

atmosphere from industries within the catchment.

These approaches will contribute to the sustenance

of aquatic resources and reduction in greenhouse

gases.These measures can also ensure the constant

supply of fish species which is a major protein

source for the human populace.

Conflict of Interest

The authors declare no conflict of interest exists.

Acknowledgment

The authors appreciate the journal editor and

anonymous reviewers for their painstaking efforts

towards reviewing this manuscript.

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