




































East


East Afr. J. Biophys. Comput. Sci. (2024), Vol. 5, Issue. 1, 51-67 
 

 

 

 

*Corresponding author: 

  Email: buchale.shishitu@yahoo.com, +251912798739 https://dx.doi.org/10.4314/eajbcs.v5i1.5S 

 
 

 

 

Ethiopia; P. O. Box 2228 

 

KEYWORDS:  

Exploitation rate; 

Growth parameters; 

Lake Abaya; 

Length at maturity; 

Mortality rates; 

Recruitment  

 

 

 

 

 

 

 

 

 

ABSTRACT 

The Nile tilapia, Oreochromis niloticus, is one of the most commercially important fish 

species in Ethiopia. Effective management is essential to sustaining their fisheries and 

providing benefits for the local communities. The study was aimed at determining the 

basic population characteristics (growth, mortality rates, and recruitment), size at first 

maturity, length at first capture, and stock status of O. niloticus in Lake Abaya. These 

basic quantitative population characteristics enable a fisheries manager to identify 

population changes resulting from fishing. The parameters were determined using length 

frequency data collected from 4089 samples of O. niloticus ranging from 23 to 47 cm in 

total length. The total length (TL) and total weight (TW) of O. niloticus samples were 

gathered between September 2021 and August 2022. The length-weight relationship 

parameters were (TW = 0.0157TL3.0192, R2 = 0.9603) and the condition factor K=1.69. The 

population parameters were determined using the ELEFAN I routine in FiSAT software. 

Estimated von Bertalanffy growth parameters were (L∞) = 49.35 cm, growth curvature (k) 

= 0.36 yr-1, age at length zero (to) = -0.40, and growth performance index (Փ') = 3.0.The 

estimated values of total natural and fishing mortalities were Z= 1.34 yr-1, M =0.34 yr-

1,and F= 1.0 yr-1, respectively. The current exploitation rate (E) was0.74, which is higher 

than the optimal (E = 0.5) and indicates that O. niloticus in Lake Abaya was 

overexploited. In order to maintain the sustainability of the fish population, it is advised 

that the local authorities establish regulations for the management of O. noloticus in Lake 

Abaya. These regulations should include protecting the use of small fishing gear and 

safeguarding fish that are caught smaller than their length at first maturity. 
 

INTRODUCTION 

The growth parameters of fish populations can 

be determined through direct evaluations of hard 

structures (otoliths, spines, or vertebrae) and 

indirect assessments based on length 

distribution data over time (Gayanilo et al., 

2002; Panfili et al., 2002). Length-based stock 

assessment tools are relatively more convenient 

in tropical and sub-tropical waters since the 

seasonal differences in the hard structures of 

these relatively warm waters are delicate and 

often present unclear band marks (Sparre and 

Venema, 1992; Panhwar and Liu, 2013). 

East African Journal of Biophysical and Computational Sciences 

Journal homepage : https://journals.hu.edu.et/hu-journals/index.php/eajbcs 
  

Hawassa University

College of Natural & Computational Sciences

Year 2021

Volume xx No xx

Length-based Estimates of Growth Parameters and Mortality Rates of Nile Tilapia (Oreochromis 

niloticus, L. 1758) in Lake Abaya, Southern Ethiopia 

 

Buchale Shishitu Shija 

 

South Ethiopia Agricultural Research Institute, Arba Minch Agricultural Research Center, Arba Minch, 

 
Research article

mailto:buchale.shishitu@yahoo.com
https://dx.doi.org/10.4314/eajbcs.v5i1.5S


East Afr. J. Biophys. Comput. Sci. (2024), Vol. 5, No. 1, 51-67 
 

52 
 

The analysis of fish stock population dynamics 

in tropical environments was made easier by the 

introduction of relative growth models and 

length-based stock assessment approaches 

(Huxley, 1993; Froese and Binohlan, 2000). 

These techniques were used to evaluate life-

history theories and produce empirical estimates 

of pertinent biological and fisheries parameters, 

including longevity and length at first maturity 

(Stergiou, 2000; Froese and Binohlan, 2000). 

Additionally, it aids in forecasting fish stock 

exploitation, which could be useful in choosing 

between different management decisions (da 

Costa and Araújo, 2003; Froese, 2006; Garcia 

and Duarte, 2006; da Costa et al., 2014; Sá-

Oliveira et al., 2015). 

Fish population biology and ecology are 

reflected in growth and mortality factors, which 

are crucial for modeling fish stock population 

dynamics. These metrics, which offer important 

evidence on the fluctuation of fish size over 

time and the reduction in population biomass 

owing to fishing and/or natural causes, are 

essential inputs for stock assessments (Pauly, 

1983; Sparre and Venema, 1998).  

Lake Abaya is one of the Rift Valley lakes in 

Ethiopia. Currently, this lake is the 4th most 

important in the country in terms of fisheries, 

contributing about 8% of capture fisheries to 

national and local markets (Gashaw and Wolff, 

2014). About four commercially important fish 

species are in Lake Abaya: Nile tilapia 

(Oreochromis niloticus), Nile perch (Lates 

niloticus), African catfish (Clarias gariepinus), 

and Bagrus docmac.  

These fish species are used as a source of 

income and livelihood for fishing communities 

in Lake Abaya. Oreochromis niloticus is the 

most significant fish species and has a great 

contribution to the annual catch and yield of 

total landings. Due to high demand for fish food 

and market prices, the fishing process takes 

place throughout the year with heavy fishing 

pressure and a continual trend of yield 

reduction. Recent and updated information 

regarding the life history and population 

dynamics of O. niloticus stock in Lake Abaya is 

lacking, despite the lake's considerable 

ecological and socioeconomic significance. A 

good understanding of fish population dynamic 

show mortality, growth, and recruitment interact 

to affect abundance is required for informed 

fisheries management. 

Even though significant stock assessment and 

population dynamics studies of O. niloticus 

stocks have been carried out in Lakes Tana 

(Workiye et al., 2019), Chamo (Buchale et al., 

2019; Million Tesfaye et al., 2021), Hawassa 

(Yosef et al., 2017), and Langeno (Genanaw et 

al., 2022), there is no comparable data regarding 

economically significant fish species in Lake 

Abaya. Therefore, this study was intended at 

determining the basic population parameters 

(growth, mortality rates, and recruitment), size 

at first maturity, length at first capture, and 

stock status of O. niloticus in Lake Abaya. The 

results can provide baseline information for 

fishery managers and scientists to design fishery 

exploitation and management strategies for 

further exploration of O. niloticus stock in the 

lake. 

MATERIALS AND METHODS 

Study area 

Lake Abaya is the second-largest lake in 

Ethiopia after Lake Tana, a highland lake, and 



East Afr. J. Biophys. Comput. Sci. (2024), Vol. 5, No. 1, 51-67 
 

53 
 

one of the two southernmost Rift Valley lakes. 

It is situated in South Ethiopia Regional State, 

between 5o55’9’’ and6o35’30’’ N latitude and 

37o36’90’’ and 38o03’45’’ E longitude (Fig. 1). 

The lake is 60 km long and 20 km wide, with a 

surface area of 1160 square kilometers. It has a 

maximum depth of 13 m and is found at an 

elevation of 1268 m, which makes it the largest 

Rift Valley Lake. This lake contains several 

islands, the greatest of which is Aruro; the 

others are Gidicho, Welege, Galmaka, and 

Alkali. Its southwest shore is home to the 

village of Arba Minch, while the southern banks 

are part of Nech Sar National Park. The 

principal perennial rivers that enter Lake Abaya 

are the Harre, Hamassa, Bilate, Gidabo, and 

Galana rivers. 

 

Figure 1. Outline map of Ethiopia with a detailed view on Lake Abaya and Chamo (Shape file 

downloaded from www.maplibrary.org) 

 

Methods of sampling and data collection 

Samples of O. niloticus were gathered from 

three cooperatives engaged in commercial 

fishing in Lake Abaya. From September 2021 to 

August 2022, samples of O. niloticus were 

randomly collected for 12 days each month at 

four commercial fishing landing sites (Ella, 

Hillo, Langama, and Gubena). Employing a 

measuring board and a sensitive electronic 

balance, the total length and total weight of 

fresh fish samples were determined to the 

nearest 0.1 cm and 0.1 g, respectively. Apart 

from the visual identification of sex by the 

examination of the gonads and abdominal 

dissection, sex was also identified by external 

features. 

 

http://www.maplibrary.org/


East Afr. J. Biophys. Comput. Sci. (2024), Vol. 5, No. 1, 51-67 
 

54 
 

Data analysis  

Length-weight relationship and condition 

factor 

The length-weight relationship was calculated 

using the power function described by Le 

Cren(1951). 

 TW = 𝑎𝑇𝐿𝑏--------------------- [1] 

 

Where,  

TW = total weight (g), a = the intercept, TL 

=total length (cm), and b = the slope of length-

weight regression  

The Fulton’s condition factor (K) is often used 

to reflect the nutritional status or well-being of 

an individual fish. It was calculated using the 

formula described by Fulton (1904), which is 

indicated below. 

 K = 
𝑇𝑊

𝑇𝐿3
∗ 100 ----------------------- [2] 

 

Where,  

K = Fulton’s condition factor 

TW = total weight of fish in grams (g)  

TL = total length of fish in (cm) 

 

Estimation of growth parameters 

The FiSAT II, ELEFAN I software’s K-scan 

technique was employed to evaluate the 

asymptotic length (L∞) and growth rate (K) 

based on the length frequency data. Using 

Pauly’s empirical formula (Pauly, 1979), the 

theoretical age at zero (to) was computed.  

 Log (-t0) = −0.3922 − 0.2752 ∗

𝐿𝑜𝑔(L∞) − 1.038 ∗ Log(k)---------------- [3] 

 

Where, 

to= is the theoretical age at which fish would 

have at zero length. 

L∞ = asymptotic length, k= von Bertalanffy 

growth constant 

Growth performance indexes were calculated by 

Munro and Pauly(1984): 

 Φ′ = 𝐿𝑜𝑔(𝑘) + 2 ∗ 𝐿𝑜𝑔(L∞)--------- [4] 

Where, Φ′ = growth performance index, k and 

L∞ are defined above 

The length at first maturity (L50) was computed 

as Froese and Binohlan’s (2000) equation: 

 

Log(L50) = 0.8979 ∗ 𝐿𝑜𝑔(L∞) − 0.0782---- [5] 

 

The longevity (A0.95)of the cohort was 

computed as (Spare and Venema, 1997). 

 

A0.95= to +
2.996

𝐾
-------------------------------- [6] 

 

Where,  

A0.95 is the age at which 95% of the cohort 

would be dead as a result of natural means; 

to = is the theoretical age at which fish would 

have at zero length;  

k = Von Bertalanffy growth constant 

Estimated mortality parameters  

Sparre and Venema (1992) state that the total 

mortality (Z) was estimated using a linearized 

length-converted catch curve. The natural 

mortality coefficient (M) was computed as 

follows using Taylor’s method: 

M = 
−ln⁡(1−0.95)

𝐴0.95
------------------------------[7] 

A0.95 indicates the age at which 95% of the 

population would die from natural causes. The 



East Afr. J. Biophys. Comput. Sci. (2024), Vol. 5, No. 1, 51-67 
 

55 
 

calculation of the fishing mortality (F) was done 

by Qamar et al. (2016). 

𝐹 = 𝑍 −𝑀 ---------------------------------- [8] 

 

Where, F = fishing mortality, Z = total 

mortality, and M = natural mortality. 

The exploitation rate (E) was calculated as 

(Georgiev and Kolarov, 1962). 

 

𝐸 = ⁡
𝐹

𝑍
-------------------------------------------- [9] 

 

The length at first capture (Lc) was estimated 

from the equation of Beverton and Holt (1957), 

which applies the growth constants of vBGF, 

the mean length of the fish catch ( ), and the 

total mortality parameter (Z): 

LC = − k(
L∞−

𝑍
) ------------------------------[10] 

 

The length at optimum cohort biomass or yield 

pre-recruitment (Lopt) was estimated from L∞, 

K, and M using the Beverton (1992) formula: 

Lopt= L∞ ∗ (
3

3+
𝑀

𝐾

)------------------- [11] 

Where, L∞, K, and M are as defined above. 

 

RESULTS AND DISCUSSIONS  

Length-weight relationship and Fulton’s 

condition factor 

The present study is conducted on a total sample 

of 4089 specimens (2344 females and 1745 

males) of O. niloticus. The monthly pooled 

length-frequency data of O. niloticus specimens 

were grouped into two-centimeter intervals. The 

obtained fish samples had lengths ranging 23 to 

47 cm (mean = 35 cm) and weights ranging 

from 190 to 1676 g (mean = 933 g). However, 

97.6% of the catches were placed in the 25-41 

cm range. The remaining 1% and 1.4% were 

less than 25cm and greater than 41cm, 

respectively. The most commonly observed 

value was the mid-length of 28 cm, which was 

followed by the length groups of 30 and 32 cm 

(Fig.2).  

 

 

Figure 2: Size structure of O. niloticus in Lake Abaya. 



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56 
 

When comparing, the maximum total length 

(47cm) observed in the present study is 

relatively smaller than those reported for the 

same fish in Lake Chamo: 57 cmTL (Yirgaw et 

al., 2000) and 53.4 cm TL (Buchale et al., 

2019), 48.5 cm TL in Gilgel Gibe I Reservoir 

(Mulugeta, 2013), and 48 cm TL in Alwero 

Reservoir (Genanaw et al., 2017). However, the 

observed maximum length for O. niloticus in 

Lake Abaya is larger than those in Lake 

Langeno, 35.5 cm TL (Genanaw et al., 2022), 

Lake Beseka (25.0 cm TL), and Lake Hawassa 

(29.0 cm TL) (Yosef et al., 2017). Fishing 

typically reduces fish size structures due to 

fishing gear selectivity and leads to fisheries-

induced evolution toward smaller sizes and 

earlier maturity (Borrell, 2013). Fish undergo a 

reduction in size and early maturation to 

replenish themselves before being eliminated by 

fishing when fishing pressure increases 

dramatically.  

The relationship between total length and body 

of O. niloticus was established with the use of a 

scatter plot diagram and power function (Fig. 3). 

The relationship was described by the equation 

TL = 0.0157TL3.0192(R2 = 0.9603, r = 0.9799). A 

strong positive correlation was found between 

the length and weight of the O. niloticus 

population in Lake Abaya, as indicated by the 

correlation coefficient (r = 0.9799). The 

regression coefficient "b" was found to be 

3.0192 when utilizing the best-fit power 

function regression to analyze the length-weight 

relationship. The power of the formula did not 

show a statistically significant deviation from 

the 3.0 hypothetical value(P > 0.05). 

This study showed an isometric growth of fish 

and a strong association between length and 

weight, as evidenced by the exponential value (b 

= 3.0192) and correlation coefficient (r = 

0.9799), respectively. Fish can grow in three 

different ways during their lives: isometric (b = 

3), negative allometric (b< 3), or positive 

allometric (b> 3), depending on the deviation of 

b (Nehemia and Maganira, 2012). When “b” is 

greater than 3, the fish increase in weight more 

than an increase in length, whereas if it is less 

than 3, the fish becomes lighter for its weight. 

However, in an isometric growth scenario, the 

fish maintains its body form as it grows longer 

(Riedel et al., 2007). The growth pattern of O. 

niloticus in Lake Abaya was isometric, based on 

the "b" value found in this investigation.  

The length-weight association of O. niloticus 

was found to be similar in some of the earlier 

studies: 3.03 in Lake Ziway (Zenebe, 1988); 

3.04 in Lake Langeno (Gashaw and Zenebe, 

2008); 3.017 in the River Nile (Shalloof and El-

Far, 2017); 3.034 in the Aulia Dam (Ahmed and 

Abdel, 2016); and 3.09 in the Lake Victoria 

cage system (Ngodhe and Owuor, 2019). 

On the other hand, some of the previously 

reported studies showed positive allometric 

length-weight relationships of O. niloticus were 

3.18 in Lake Chamo (Buchale, 2020), 3.16 Lake 

Victoria (Ngodhe and Owuor, 2019), 3.19 in 

Lake Ziway (Gashaw and Zenebe, 2008), and 

3.366 in Lake Naivasha (Keyombe et al., 2017) 

while, 2.33 in Lake Naivasha (Cishahayo et al., 

2022), 2.89 in Lake Langeno (Genanaw et al., 

2022), 2.76 in Alwero Reservoir (Genanaw et 

al., 2017), and 2.934 in Lake Ardibo (Endalk et 

al., 2018) were some of the negative allometric 

length-weight relationships of O. niloticus. 

The value of b can vary annually due to a 

variety of factors, such as season, habitat, gonad 

maturity, sex, nutrition, stomach fullness, 



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57 
 

health, preservation techniques, and 

environmental circumstances (Bagenal and 

Tesch, 1978; Arslan et al., 2004; Froese, 2006; 

Yilmaz et al., 2012; Ali et al., 2016). 

Furthermore, disparities in fish growth patterns 

could also be associated with the species’ 

condition, phenotype, environment, and specific 

geographic region (Tsoumani et al., 2006). 

 

Figure 3: Length-weight relationship of pooled O. niloticus in Lake Abaya.

Fulton’s condition factor 

As depicted in Table 1, the monthly mean 

values of Fulton's condition factor (K) for 

females, males, and combined sexes ranged 

from 1.58 to 1.77. For females, males, and 

combined sexes, the average K value was 1.70, 

1.68, and 1.69, respectively. In O. niloticus of 

Lake Abaya, there was statistically no 

significant variation in K between the sexes or 

with the month's interaction (P > 0.05). 

The condition factor is a metric that represents 

the fish's physiological state with respect to 

feeding, spawning, and other elements of their 

overall health. According to Blackwell et al. 

(2000), high condition factor values imply 

advantageous environmental conditions 

(including habitat and prey availability), while 

low values suggest less conducive 

environmental conditions. The ecological 

habitat of fish species can be evaluated using the 

condition factor, which is also highly affected 

by biotic and abiotic environmental factors 

(Ayoade, 2011; Onimisi and Ogbe, 2015; Abu 

and Agarin, 2016).Five categories were created 

by Morton and Routledge (2006) based on the K 

values: very bad (0.8–1.0), bad (1.0–1.2), 

balance (1.2–1.4), good (1.4–1.6), and very 

good (> 1.6). However, according to Ayoade 

(2011), a fish in good health has a condition 

factor greater than one. 

In this study, the average value of the condition 

factors were 1.70, 1.68, and 1.69 for females, 



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58 
 

males, and combined sexes, respectively. As 

previously mentioned, the condition factor in 

this study was more than 1.6, indicating that O. 

niloticusin Lake Abaya is doing quite well. 

 

Table 1. Mean monthly condition factor of females, males and combined O. niloticus 
Months Females Males Combined sexes 

Sep-21 1.58 1.60 1.58 

Oct-21 1.68 1.66 1.67 

Nov-21 1.63 1.63 1.63 

Dec-21 1.61 1.59 1.60 

Jan-22 1.67 1.62 1.65 

Feb-22 1.71 1.69 1.70 

Mar-22 1.73 1.67 1.70 

Apr-22 1.71 1.71 1.71 

May-22 1.77 1.75 1.76 

Jun-22 1.76 1.72 1.74 

Jul-22 1.77 1.77 1.77 

Aug-22 1.75 1.75 1.75 

Average 1.70 1.68 1.69 

 

Estimated growth parameters 

The O. niloticus in Lake Abaya was predicted to 

have von Bertalanffy growth parameters of 

asymptotic length (L∞) and annual growth 

constant (k) of 49.35 cm and 0.36 yr-1, 

respectively. The estimated theoretical age at 

birth (to) was -0.40. The longevity (A0.95), the 

age at which 95% of the population would be 

dead as a result of natural means, was 8.72 

years. The growth performance index Phi (Փ’) 

was estimated at 3.0 (Fig. 4). 

The estimated value of L∞ in this study was 

lower than the estimates from the studies in 

Lakes Chamo, 55.0 cm (Buchale et al., 2019), 

and Victoria, 58.8 cm (Njiru et al., 2004). 

However, compared to Lakes Langeno (35.7 

cm; Genanaw et al., 2022), Tana (44.1 cm; 

Workiye et al., 2019), Victoria (46.24 cm; 

Yongo et al., 2018), Koka (44.5 cm; Gashaw, 

2016), and Naivasha (42.0 cm; Waithaka et al., 

2020), the L∞ in this study was higher. 

It is possible that the various water bodies' 

varying environmental conditions account for 

the differences in estimations of the von 

Bertalanffy growth parameters (L∞ and k) when 

compared to similar research. The size of the 

population and the ways in which fish adjust 

throughout their lives are other elements that 

influence growth. As noted by Sparre and 

Venema (1998), this could potentially vary 

throughout stocks and species and be impacted 

by various methods of analysis. Similar species 

can have different growth rates in different 

habitats (Lowe-McConnell, 1982). The length 

characteristics (TLmax and L∞) may be 

influenced by genetics, resource availability, 

and population density. The fishing pressure is 

also a factor for change in the asymptote length 

(L∞) of fish in a given water body. If fishing 

gear is discriminatory and oriented toward 

harvesting larger fish, they may become rare in 



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59 
 

overexploited fisheries, and the scarcity of these 

fish in a given sample will certainly 

underestimate the growth parameters. 

In comparison to Lakes Langeno, 2.61 

(Genanaw et al., 2022), Ziway, 2.76 (Gashaw, 

2006), and Naivasha, 2.57 (Waithaka et al., 

2020), the estimated growth performance index 

(Փ’ = 3.0) in the current study was greater. On 

the other hand, the index in the present study 

was lower than in Lakes Chamo, 3.16 (Million 

et al., 2021), and Victoria, 3.14 (Yongo et al., 

2018). The most effective method for 

determining the average growth parameters of a 

specific species is to use the growth 

performance index, which should show 

comparable values when comparing several 

groups within the same species (Gulland, 1983; 

Sparre and Venema, 1998). The availability of 

food, environmental factors, and fishing 

pressures can all have an impact on a fish 

species' growth performance index, in addition 

to the genetic composition that dictates the 

species' potential for growth (Getabu, 1992). 

O. niloticus in Lake Abaya had an approximate 

lifespan of 8.72 years, which is comparable to 

the lifespan of O. niliticus in Lake Langeno, 8.9 

years, as indicated in Genanaw et al. (2022). 

Both biological elements and environmental 

factors can have an impact on lifespan. Fish life 

spans are significantly influenced by a variety of 

biological criteria, including sex, genetic 

makeup, diet, reproduction, age, and maturation, 

in addition to environmental influences 

including salinity, temperature, and predation 

(Das, 1994). 

 

Figure 4: ELEFAN I K-Scan routine FiSAT II output for O. niloticus in Lake Abaya. 



East Afr. J. Biophys. Comput. Sci. (2024), Vol. 5, No. 1, 51-67 
 

60 
 

Estimated mortality parameters  

In order to estimate total mortality, a length 

composition data set was created and prepared 

for a linear regression analysis between the X 

and Y variables (Table 2). The mortality 

parameters were determined using a linearized 

length-based catch curve analysis. As indicated 

in figure 5, the slope of the regression line (b) is 

-1.34, and hence, the estimated total mortality 

rate (Z) was 1.34 yr-1. The natural mortality rate 

(M) and fishing mortality rate (F) were 0.34 yr-

1and 1.0 yr-1, respectively. Using these mortality 

estimates, the exploitation rate (E) was 

computed as 0.74, which indicates that O. 

niloticus in Lake Abaya is overexploited. 

 

Table 2: Parameters for length-based catch curve analysis 

Length group 

(cm) 
     x y 

Catch k L∞ (cm) ∆t (L1,L2) (L1+L2)/2 t(L1+L2)/2 Ln(C(L1,L2)/∆t) 

23-25 40 0.36 49.35 0.22 24 1.85 5.21 

25-27 450 0.36 49.35 0.24 26 2.08 7.54 

27-29 884 0.36 49.35 0.26 28 2.33 8.13 

29-31 752 0.36 49.35 0.29 30 2.60 7.87 

31-33 529 0.36 49.35 0.32 32 2.90 7.41 

33-35 394 0.36 49.35 0.36 34 3.24 6.99 

35-37 279 0.36 49.35 0.42 36 3.63 6.51 

37-39 237 0.36 49.35 0.49 38 4.08 6.18 

39-41 243 0.36 49.35 0.60 40 4.62 6.01 

41-43 222 0.36 49.35 0.76 42 5.29 5.68 

43-45 57 0.36 49.35 1.05 44 6.17 3.99 

45-47 2 0.36 49.35 1.71 46 7.47 0.16 

Total  4089       

 

Fish mortality can be attributed to both natural 

and anthropogenic factors. Most of the natural 

mortality could be attributed to old age, 

diseases, and predation factors in the aquatic 

ecosystem. The natural mortality (M) in the 

present study is lower than the fishing mortality 

(F), and indicating that the primary cause of 

mortality for O. niloticus in Lake Abaya is 

attributed by fishing factors. When comparing 

the estimated values for mortality (F ˃M), it is 

possible to conclude that fishing mortality was a 

more important source of mortality for O. 

niloticus in Lake Abaya. 

A population dominated by mortality was also 

indicated by the Z/K ratio of 3.72 estimated in 

this study. The population is growth-dominated 

if the ratio Z/K is less than 1, mortality-

dominated if it is greater than 1, and in 

equilibrium when growth and mortality are 

equal if it is equal to 1. If Z/K = 2 in a 

mortality-dominated population, the population 

is considered lightly exploited (Beverton and 

Holt, 1957). According to Beverton and Holt’s 

(1957) general criteria, the Z/K value in the 

current study indicated that the O. niloticus 

population in Lake Abaya was highly exploited. 

This is also revealed by the estimated high 



East Afr. J. Biophys. Comput. Sci. (2024), Vol. 5, No. 1, 51-67 
 

61 
 

exploitation rate (E= 0.74), which indicates a 

state of overexploitation. As per Gulland’s 

(1971) assumptions, a sustainable yield is 

considered optimal if F is equal to M or if the 

rate of exploitation (E) is 0.5. If E>0.5, it is 

typically assumed that the stock has been 

overexploited. 

 

 

 

Figure 5: Linearized length-based catch curve of O. niloticus in Lake Abaya. 

Length at first maturity (L50) and length at first 

capture (Lc) were 27.68 cm and 27.97 cm, 

respectively. The optimum length (Lopt) of O. 

niloticus in Lake Abaya was also estimated at 

37.53 cm. The L50 and Lc of O. niloticus in 

Lake Abaya were almost similar and vulnerable 

for fishing. Based on the evidence shown in this 

study, O. niloticus in Lake Abaya is ready to be 

removed through fishing at its first spawning 

stage. Catching fish with total length less than 

or equal to L50 is the main cause of overfishing, 

and it is recommended that the mesh size of 

fishing nets used in Lake Abaya should be 

increased to catch fish above 28 cm for 

conservation of the stock. When the results of 

virtual population analysis (VPA) were 

considered (Fig. 6), fish with total a length of 

27-32cm had more exposure to fishing gear, 

whereas fishing mortality was higher for fish 

with a total length above 39 cm. 

 

y = -1.3411x + 11.571

R² = 0.9072

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0

L
n

(C
(L

1
,L

2
)/

∆
t)

Average age t(L1+L2)/2



East Afr. J. Biophys. Comput. Sci. (2024), Vol. 5, No. 1, 51-67 
 

62 
 

 

Figure 6: Estimated virtual population of O. niloticus in Lake Abaya. 

The length at first maturity (L50) of O. niloticus 

in the present study was higher than that of 

Lakes Chamo 23.6 cm (Buchale et al., 2021), 

Hayq 12.8 cm for females and 12.9 cm for 

males (Tessema et al., 2019), Langeno 16.62 cm 

(Genanaw et al., 2022), and Hawassa 20.8 cm 

for females and 20.3 cm for males (Muluye et 

al., 2016). According to Fryer and Iles (1972) 

and Lowe-McConnell (1987), the size of 

maturation varies depending on demographic 

conditions and is influenced by both genes and 

environment. Depending on the fishery's 

selectivity, fishing pressure can affect 

population structure, growth, and early 

maturation (Jorgensen et al., 2007). In water 

bodies with high fishing pressure, the fish 

devote more resources to reproduction than to 

somatic body building (Bandara and 

Amarasinghe, 2018). According to Jonsson et 

al. (2014), fish that live in harsh situations also 

exhibit early sexual maturity since this is a 

coping mechanism for maintaining maximal 

reproduction under stressful conditions. 

Figure 7: The seasonal recruitment pattern of 

O. niloticus in Lake Abaya. 

Estimated seasonal recruitment pattern and 

relative yield per recruitment 

The estimated recruitment pattern of O. 

niloticus in Lake Abaya was year-round, with 

one peak period (May)in the year (Fig. 7). 

Recruitment adds younger fish to the fishery 



East Afr. J. Biophys. Comput. Sci. (2024), Vol. 5, No. 1, 51-67 
 

63 
 

and can vary from year to year by orders of 

magnitude.  

The peak recruitment season of O. niloticus in 

the present study was similar to O. niloticus in 

Lake Langeno, as indicated in Genanaw et al. 

(2022). The biology of tropical freshwater fish 

reproduction appears to be significantly 

influenced by patterns of rainfall and variations 

in water levels (Wootton, 1990). The monthly 

average rainfall in the study area is higher in 

April, May, and October, which could be one of 

the probable reasons for the occurrence of a 

peak recruitment pattern for O. niloticus in May.  

 

Figure 8: Beverton and Holt's relative yield per recruitment curve for O. niloticus in Lake Abaya.

The degree to which the current rate of 

exploitation is optimal, below optimal, or 

excessive with respect to the population’s 

capacity for self-renewal was ascertained using 

the correlation curve between the exploitation 

rate and yield per recruitment. According to the 

estimated yield per recruitment curve, the 

current exploitation rate was 0.74 with a yield 

per recruitment of 0.029, the optimal 

exploitation rate (Eopt) was 0.5 with a yield per 

recruitment of 0.05, and the exploitation rate for 

maximum yield (Emax) was 0.421 with a yield 

per recruitment of 0.053 (Fig. 8). It is evident 

that the O. niloticus population in Lake Abaya 

was overexploited because the present 

exploitation rate was higher than the ideal 

exploitation rate. In order to monitor and set 

regulations for O. niloticus fishing in Lake 

Abaya, the local authorities need to be aware of 

this circumstance. The fish that are collected 

should be the same size at which they have 

spawned, in order to preserve the sustainability 

of the fish population. 

CONCLUSIONS & RECOMMENDATIONS 

The growth pattern of O. niloticus in Lake 

Abaya was isometric, which implied that an 

increase in body length is proportional to body 

weight. The groups with mean lengths ranging 

from 25 cm to 41 cm accounted for 



East Afr. J. Biophys. Comput. Sci. (2024), Vol. 5, No. 1, 51-67 
 

64 
 

approximately 97.6% of the total capture and 

significantly influenced the yield of fish. The 

average value of Fulton’s condition factor was 

1.70, 1.68, and 1.69 for females, males, and 

combined sexes, respectively, which implied 

that the wellbeing of O. niloticus in Lake Abaya 

was in a very good health condition.  

This study generated important information on 

population dynamics parameters (growth, 

mortality rates, and recruitment) and other 

crucial life-history characteristics that can serve 

as basic stock assessment tools for O. niloticus 

management in Lake Abaya. The asymptotic 

length (L∞) and growth rate (k) were 49.35 cm 

and 0.36 per year, respectively. The length at 

first capture (Lc) was estimated at 27.97 cm, 

while the length at first maturity (L50) was 

estimated at 27.68 cm. Based on the result, 

premature harvesting of fish (i.e. before 

reaching the intended size or first maturity) may 

alter the recruitment potential of the stock, 

which in turn may result in the exhaustion or 

collapse of the stock. The long lifespan in which 

95% of the population would be dead as a result 

of natural means was 8.72 years.  

Moreover, the current exploitation rate (E= 

0.74) is higher than the optimal (E = 0.5) and 

indicates that O. niloticus in Lake Abaya was 

overexploited. Based on results of this study, it 

is suggested that the fisheries management of 

the lake should include controlling or restricting 

the usage of small fishing gear in addition to 

reducing fishing efforts to ensure the 

sustainability of this commercially important  

Acknowledgements 

The author is grateful to the Southern 

Agricultural Research Institute for providing 

financial support, the Arba Minch Agricultural 

Research Center for allowing access to the 

necessary facilities, and livestock research staff 

members for their valuable assistance during the 

execution of the experiment. 

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