East East Afr. J. Biophys. Comput. Sci. (2023), Vol. 4, Issue. 1, 28-41 *Corresponding author: Email: buchale.shishitu@yahoo.com https://dx.doi.org/10.4314/eajbcs.v4i1.3S Buchale Shishitu Shija*, and Atnafu W/yohans Firew Fisheries and Aquaculture, Southern Agricultural Research Institute (SARI), Arba Minch Agricultural Research Center, P.O.Box 2228, Arba Minch, Ethiopia KEYWORDS: Ayalew reservoir; Cohort analysis; Condition factor; Cyprinus carpio; Length-weight relationship; Population dynamics parameters ABSTRACT Common carp (Cyprinus carpio) is an imported fish species in Ayalew reservoir. The study was intended at estimating important population dynamics parameters and production potential in the reservoir. Total length (TL) and total weight (TW) data were collected from a total of 276 fish samples (141 females and 135 males). The obtained data were analyzed using FiSAT II software. The population and production potential were assessed by using Jones length based cohort analysis model and length-based Thompson and Bell yield prediction models. The average total length was 26 cm and the dominant length groups ranged from 17 to 33 cm were 87%. The length-weight relationship parameters were (TW = 0.0565TL2.53, R2 = 0.95) and the condition factor K = 1.29. The parameters of von Vertalanffy growth curve were L∞ = 41 cm, k = 0.52, to = -0.29, θ = 2.9 and A0.95 = 5.5 years. The assessed values of the total, natural and fishing mortalities were Z = 1.23, M = 0.55 and F = 0.68, respectively. The current exploitation rate, 0.55, indicates slightly overexploitation. The estimated fish population and the annual fish yield were 59,304 and 1.5 tons, respectively. However, investigation on reproductive biology, limnolocal aspects and stock enhancements should be required for the sustainability of these resources. INTRODUCTION Common carp (Cyprinus carpio) is considered to be a very important aquaculture species in many Asian and some European countries. It is widely distributed and frequently considered a nuisance species outside its native range (Penne and Pierce, 2008; Mohammad, 2015). The Common carp is one of the most common freshwater fish invaders worldwide, creating adverse effects on water quality and impacting ecosystem structure and function (Letvin et al., 2017). It is highly adaptable to new environments and can alter the biotic and abiotic integrity of aquatic ecosystems (Bajer et al., 2012). 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 Population Dynamics and Yield Estimation of Common Carp (Cyprinus carpio, Linnaeus, 1758) in Ayalew Reservoir, Gamo Zone, Southern Ethiopia Research article mailto:buchale.shishitu@yahoo.com https://dx.doi.org/10.4314/eajbcs.v4i1.3S East Afr. J. Biophys. Comput. Sci. (2023), Vol. 4, No. 1, 28-41 29 In Ethiopia, Cyprinus carpio was first introduced to Aba Samuel Dam (Awash River basin) in 1940 from Italy (Getahun, 2017). Later, it has been introduced in Lake Ziway in the late 1980s (FAO, 1997; Abera et al., 2015). For food security purpose, C. carpio was introduced in highland lakes such as Ashengie, Ardibo, and Maybar and the introduction was successful (Golubtsov and Darkov, 2008). Ayalew reservoir is one of the highland water bodies in Gamo Zone Chencha woreda. According to gathered information from Chencha woreda, C. carpio was introduced in Ayalew reservoir in the late 1980s by National Fishery and Aquatic Life Research Center. After some years later, the fish was adapted and has been observed by the local community in the reservoir. Since then, there was no any documented information about the population dynamics of C. carpio in Ayalew reservoir. For sustainable management and utilization, information on the population dynamics and production potential is very important for this species. Therefore, the objective this study was to estimate the population dynamics and production potential of C. carpio in the reservoir. MATERIALS AND METHODS Description of the study area Ayalew reservoir is found in Chencha woreda in Gamo Zone. It is situated at the coordinates of 06°25′068″N latitude and 037°57′368″E longitude with an elevation of 2861 meters above sea level. The area of the reservoir is about 4.37 ha or 0.0437 km2 with a maximum depth of 5.3 meters. . Figure 1. Location of Ayalew reservoir in Chencha woreda, Gamo zone, Southern Ethiopia East Afr. J. Biophys. Comput. Sci. (2023), Vol. 4, No. 1, 28-41 30 Methods of sampling and data collection Ten youths were trained about fish catching and data recording systems. For the trained youths a boat and four monofilament gill nets with 3.5 mesh sizes were delivered for the research activities. Two data collectors were selected based on their skill and involved in data collecting process. The gillnets were deployed with plastic bottles as floats across the reservoir at 5:00 PM and collected at 7:00 AM the next morning. Immediately following capture, the total length (TL) and weight (TW) of the fish were measured, using a measuring board and sensitive balance, to the nearest 0.1 cm and 1 g, respectively. Data were collected weekly for eight months, from October 2021 to May 2022. Length-weight relationship and condition factor Length-weight relationship was calculated using power function (Le Cren, 1951). TW = 𝑎𝑇𝐿𝑏------------------------------------------ [1] Where, TW = total weight (g), a = the intercept, TL = total length (cm), 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 by using the formula described by Fulton (1904) which indicated below. K = 𝑇𝑊 𝑇𝐿3 ∗ 100----------------------------------------- [2] Where, K = Fulton’s condition factor TW = total weight of fish in gram (g) TL = total length of fish in centimeter (cm) Data summarization and analysis Catch data were compiled and summarized in a format suitable for the Jones length-based cohort analysis and the length-based Thompson and Bell yield prediction models. Microsoft Office Excel (2010) was employed for both data summarization and subsequent analysis. Estimating growth parameters The K-scan technique available in the ELEFAN I module of the FiSAT II software was utilized to estimate asymptotic length (L∞) and growth rate (k) from the length-frequency data. Pauly's empirical formula (1979) was then applied to calculate the theoretical age at length zero (t0) Log (–to) = –0.392 – 0.275*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 rate constant Growth performance indexes were calculated as Munro and Pauly (1983): θ = log(k) + 2 × log(L∞) ----------------------- [4] Where, θ = growth performance index The length at first maturity (L50) was computed as Froese and Binohlan’s (2000) equation: log(L50) = 0.8979 × log(L∞) – 0.0782 ------------- [5] East Afr. J. Biophys. Comput. Sci. (2023), Vol. 4, No. 1, 28-41 31 Estimating mortality parameters based on length composition data For the estimation of total mortality rates, linearized length converted catch curve method was applied and the mortality parameters were calculated based on the following formula (Pauly, 1984). ∆t = 1/k*Ln[(L∞ - L1)/[(L∞ - L2)] -----------------[6] t (L1+L2)/2 = -1/k{Ln[(1-(L1+L2)/2/(L∞)] --------[7] Ln{[C(L1, L2)]/[∆t(L1, L2)]} = a- Z*t(L1+L2)/2 ---[8] Where: ∆t = is age interval between L1 and L2 or the time taken by fish of length L1 to reach length L2 t(L1+L2)/2 = age of the average consecutive length groups (X variable) Ln{[C(L1,L2)]/[∆t(L1, L2)]} = Y variable The natural mortality coefficient (M) was estimated using Taylor’s method (1958) as follows: M = -ln(1-0.95)/A0.95------------------------------ [9] Where, A0.95 = longevity, the age at which 99% of the cohort would be dead as a result of natural means (Spare and Venema, 1997). A0.95 = to+2.996/k ------------------------------- [10] Where, to = is the theoretical age at which fish would have at zero length; k = von Bertalanffy growth rate constant To obtain total mortality, regression analysis was conducted between X and Y variables as described in formula 7 and 8, respectively. Total mortality (Z) = fishing mortality (F) + natural mortality (M) ---------------------------------------- [11] Then, the fishing mortality rate (F) was obtained by subtracting M from Z. Estimating population sizes and fishing mortalities by length group (Jones, 1984) Jones length-based cohort analysis model was used to estimate the population size and fishing mortality coefficient of C. carpio by length groups. This was done in the following three steps: i) Population number estimate of the largest length group in the catch. N(largest L) = C(Largest L)*(Z Largest L/F Largest L ) ----------------------------------------------------- [12] Where, N (largest L) = the population of the largest length group in the catch; C(largest L) = the catch of the largest length group; Z(largest L) = the total mortality rate of the largest length group in the catch; F(largest L) = the fishing mortality rate of the largest length group in the catch; C(L1, L2) = the catch of the length groups of N(L1) ii) Population numbers estimate of consecutively younger length groups in the catch. N(L1) = [N(L2) * H(L1, L2) + C(L1, L2)] * H(L1, L2) - ---------------------------------------------------------- [13] Where, N(L1) = The population number of L1 (younger) fish N(L2) = The population number of L2 (older) fish East Afr. J. Biophys. Comput. Sci. (2023), Vol. 4, No. 1, 28-41 32 H(L1, L2) = the fraction of N(L1) fish that survived natural death as it grows from length L1 to L2 and computed as the following equation (Jones, 1984). H(L1, L2) = [(L∞ - L1)/ (L∞ - L2)] (M/2k) ------- [14] Where, L∞ = the asymptotic length (cm) of C. carpio attained at mature size; L1 and L2 = consecutive length groups of fish (cm) that contributed to the fishery; K = von Bertalanffy growth rate constant (yr-1); M = the rate of natural mortality coefficient iii) Fishing mortality rate estimate of the respective length groups. Fishing mortality values for each length group was estimated using the equation as follows: F(L1, L2) = (1/∆t) * ln[N(L1)/N(L2)] – M -------- [15] Where, F(L1, L2) = Fishing mortality coefficient pertaining to the respective length group; N(L1), N(L2) and M are as defined above. To know the status of the stock, the exploitation rate (E) was estimated from mortality parameters as: E = F/Z. The exploitation rate (E) equal to 0.5 is considered as optimum level of exploitation; whereas less than 0.5 refers to under exploitation and greater than 0.5 refers to overexploitation (Gulland, 1971). Thompson and Bell (1934) yield prediction procedure Step 1) Estimating the total annual yield obtained under the current level of fishing i) Estimating the yield obtained per year from each length group Yield from each length group obtained per year (Y(L1, L2) - is catch in number per length group per year (C(L1, L2) multiplied by the average weight of each length group i.e., Y(L1, L2) = C(L1, L2) * W(L1, L2) --------------- [16] Where, Y(L1, L2) = the yield (weight) of fish obtained per year from respective length group; C(L1, L2) = total annual catch of fish obtained from respective length group; W(L1, L2) = the mean weight of each length group estimated using equation W(g) = a* Lb ---------------------------------------- [17] Where, W(g) = the average weight of each length group, L = the average length (cm) of each length group i.e., L = (L1+L2)/2 in which L1 and L2 are the length intervals of consecutive length groups. ‘a’ and ‘b’ are values of the regression coefficients. ii. Estimated yield was obtained from all length groups per year by adding up the contribution of each length group. RESULTS AND DISCUSSION Length-weight relationship and Fulton’s condition factor The values of the regression coefficient “b” for females (n =141), males (n =135) and combined sexes (n =276) obtained from the length-weight relationship by using the best-fit regression of power function gave 2.51, 2.47 and 2.53, respectively (Fig. 2). Analysis of variance (one- way ANOVA) showed the significant differences between the regression coefficient “b” and the cubic value of “b” (3) (P < 0.05). East Afr. J. Biophys. Comput. Sci. (2023), Vol. 4, No. 1, 28-41 33 As indicated in Table 1, the t-test also revealed that the presence of significant difference between the regression coefficient “b” in female, male and combined sexes (P < 0.05). Females TW = 0.0611TL2.51 R² = 0.9418 n = 141 0 100 200 300 400 500 600 700 0 10 20 30 40 50 T o ta l w ei g h t (g ) Males TW = 0.0664TL2.47 R² = 0.9324 n = 135 0 100 200 300 400 500 600 0 10 20 30 40 50 T o ta l w ei g h t (g ) East Afr. J. Biophys. Comput. Sci. (2023), Vol. 4, No. 1, 28-41 34 Figure 2. Length-weight relationship of C. carpio from Ayalew reservoir In this study, the length-weight relationship showed that C. carpio showed a negative allometric growth. Values of b equal to 3 indicate that the fish grows isometrically; values other than 3 indicate allometric growth (Tesch, 1971). The b value is often 3.0 and generally between 2.5 and 3.5 (Froese, 2006). The b values in fish is species specific and therefore varies with sex, age, seasons, physiological conditions, growth increment and nutritional status of fish (Ricker, 1975; Bagenal and Tesch, 1978). Mert and Bulut (2014), Saylar and Semra (2014), Yilmaz et al. (2012), Ünver and Yildirim (2011), and Richard et al. (2018) also reported negative allometric growth for C. carpio in different water bodies worldwide, and their b values were 2.9, 2.8, 2.83, 2.89 and 2.75, respectively. However, positive allometric growth values for this species were documented by (Karataş et al., 2007; Kirankaya and Ekmekçi, 2004), who reported b values of 3.21 and 3.022, respectively. The difference in the results of the current and previous publication might possibly be associated with reasons related to ecosystem and biological phenomena such as seasons, feeding behavior, cmpetition for fd, and maturity stages. Table 1. Regression static parameters of C. carpio. Parameters Female Male Combined a value 0.0611 0.0664 0.0565 b value 2.5135 2.4698 2.5303 Std. Error (Sb) 28.61 30.05 29.58 R2 0.9418 0.9324 0.9451 t-calculated 48.62 42.55 69.26 t-critical (5%) 1.97 1.97 1.97 No of observation 141 135 276 Significance 0.000 0.000 0.000 Combined TW = 0.0565TL2.53 R² = 0.9451 N = 276 0 100 200 300 400 500 600 700 0 10 20 30 40 50 T o ta l w ei g h t (g ) Total length (cm) East Afr. J. Biophys. Comput. Sci. (2023), Vol. 4, No. 1, 28-41 35 Monthly mean Fulton’s condition factor (K) ranged from 1.15 to 1.39 for females, 1.16 to 1.44 for males and 1.23 to 1.38 for combined sexes (Table 2). The average K value for females, males and combined sexes were 1.28, 1.30 and 1.29, respectively. Females exhibited their lowest condition factor (1.15) in February and their highest (1.39) in May. In contrast, the lowest value for males (1.16) was recorded in November, and the peak value (1.44) occurred in April. For combined sexes, the minimum value (1.23) was recorded in November and the highest (1.38) in April and May. Fulton’s Condition factor indices have been widely used as indicators of relative health and depend on the environmental conditions and food availability. April and May are highly rainy season in the study area and might be the reason for the variation in monthly condition factor. The results indicated that there was no significant difference between sexes as well as month’s interaction in mean condition factor of C. carpio (P > 0.05). Table 2.The mean monthly Fulton’s condition factor of female, male and combined C. carpio in Ayalew reservoir. Moths Female Male Combined October 1.28 1.21 1.25 November 1.33 1.16 1.23 December 1.23 1.30 1.26 January 1.28 1.38 1.33 February 1.15 1.32 1.25 March 1.22 1.25 1.24 April 1.32 1.44 1.38 May 1.39 1.36 1.38 Average 1.28 1.30 1.29 Morton and Routledge (2006) divided the K values into five categories as 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). On the other hand, Ayoade (2011) suggests that the Fulton’s condition factor higher than one is a good fish health condition. Based on the five categories above, the condition factor in the present study was not in the range of 1.4-1.6 and C. carpio in the reservoir was not in a good health condition. This might be due to anthropogenic factor that affects the limnological aspects of the reservoir. The length cmpsition of sampled catch and estimated annual catch of C. carpio The mean total length catch composition and yield contribution of C. carpio are indicated in Fig. 3. The compositions were ranged from 13 to 39 cm with an average length of 26cm. The mean length ranged from 17 cm to 33 cm were about 87% of the total catch and had a high contribution in fish yield. East Afr. J. Biophys. Comput. Sci. (2023), Vol. 4, No. 1, 28-41 36 Figure 3. Size structure of C. carpio harvested from Ayalew reservoir Estimation of growth and mortality parameters The estimated asymptote length (L∞) and the length at first maturity (L50) were 41 cm and 23.44 cm, respectively. The annual growth constant (k) and the growth performance index Phi (θ) were 0.52 yr-1 and 2.9, respectively (Fig. 4). The theoretical age at which fish would have at zero length (to) was -0.29 and the Longevity (A0.95), the age at which 99% of the cohort would be dead as a result of natural means was 5.5 years. The fish species with a growth constant (k) value greater than or equal to one (1) is a fast growing fish species (Gulland, 1983; Sparre and Venema, 1998). Besides the genetic makeup which determines the growth potential of the fish species, food availability, environmental conditions and fishing effects could affect the growth performance index of a particular fish species (Getabu, 1992). According to Sambo and Haruna (2012), the growth performance index is a function of L∞ in which increase in L∞ leads to an increase in the growth performance index. The growth constant (k) value ranges from 0.06 to 0.48, 0.12 to 0.75 and 0.11 to 0.69 for C. carpio species that live naturally in rivers, lakes and reservoirs, respectively (Fish Base, 2011). The growth constant (k) of C. carpio in Ayalew reservoir was 0.52 yr-1and laid into the range of 0.11-0.69 for the C. carpio fish populations that live naturally in water of reservoirs. 0 2 4 6 8 10 12 14 13 15 17 19 21 23 25 27 29 31 33 35 37 39 C at ch c o m p o si ti o n ( % ) Mean total length (cm) East Afr. J. Biophys. Comput. Sci. (2023), Vol. 4, No. 1, 28-41 37 Figure 4. ELEFAN I K- scan routine FiSAT II output for C. carpio from Ayalew reservoir. A length composition data prepared for a linear regression analysis was established between X and Y variables for estimation of total mortality (Table 3). Table 3. Parameters for length-based catch curve analysis Length group (cm) Annual Catch C(L1,L2) X Y k L∞ (cm) ∆t (L1,L2) (L1+L2)/2 t(L1+L2)/2 Ln(C(L1,L2)/∆t) 16-18 859 0.52 41 0.16 17 1.03 8.59 18-20 967 0.52 41 0.17 19 1.20 8.62 20-22 859 0.52 41 0.19 21 1.38 8.40 22-24 564 0.52 41 0.21 23 1.58 7.88 24-26 591 0.52 41 0.24 25 1.81 7.81 26-28 967 0.52 41 0.28 27 2.07 8.16 28-30 644 0.52 41 0.32 29 2.36 7.60 30-32 591 0.52 41 0.39 31 2.71 7.33 32-34 483 0.52 41 0.48 33 3.14 6.91 34-36 349 0.52 41 0.65 35 3.70 6.29 36-38 107 0.52 41 0.98 37 4.48 4.69 38-40 81 0.52 41 2.11 39 5.81 3.64 Based on the linearized length-based catch curve analysis, the mortality parameters were estimated. As indicated in figure 5, the slope of the regression line (b) is -1.23 and hence, the estimated total mortality rate (Z) was 1.23 yr-1. Out of the total mortality, natural mortality rate (M) and fishing mortality rate (F) were 0.55 yr-1 and 0.68 yr-1, respectively. Using these mortality estimates, the exploitation rate (E) was computed as 0.55 and indicates that the C. carpio is slightly overexploited. East Afr. J. Biophys. Comput. Sci. (2023), Vol. 4, No. 1, 28-41 38 Based on the computed exploitation rate (E), the C. carpio in Ayalew reservoir was overexploited and the fish population was not abundant enough to utilize the resource sustainably. The exploitation rate (E) of a fish stock is at its maximum level and sustainable if the value of F was equivalent with or same with the value of M or the rate of exploitation (E) had value of 0.5 (Gulland, 1983). Figure 5. Linearized length-based catch curve of C. carpio from Ayalew reservoir Estimated population sizes and current yield by length group The estimated population number and annual yield of C. carpio in the study area were about 59, 304 and 1.5 tons, respectively (Table 4). The recruitment pattern of the fish was year-round with two peak recruitment period in the year. The peak recruitment takes place twice a year, in April and July to August for this particular fish species (Figure 6). The projected annual recruitment of C. carpio in the reservoir was about 9,118 as indicated in Table 4 (column 8; row 3). Based on the estimated population, it is possible to obtain about 7.4 tons of fish biomass and 1.5 tons of fish yield per year. y = -1.2259x + 10.586 R² = 0.9805 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 L n (C (L 1 ,L 2 )/ ∆ t) Average age t(L1+L2)/2 East Afr. J. Biophys. Comput. Sci. (2023), Vol. 4, No. 1, 28-41 39 Figure 6. Recruitment pattern output from FiSAT II for C. carpio in Ayalew reservoir Table 4. Estimated population, current yield and other parameters of C. carpio by length group Length group (cm) Annual catch x y ∆t (L1,L2) (L1+L2)/2 t(L1+L2)/2 Ln(C(L1,L2)/∆t) H N(L1,L2) Current yield (kg) 12-14 161 0.14 13 0.73 7.07 1.01 9118 6 14-16 322 0.15 15 0.88 7.69 1.01 8774 18 16-18 859 0.16 17 1.03 8.59 1.01 8264 64 18-20 967 0.17 19 1.20 8.62 1.01 7220 95 20-22 805 0.19 21 1.38 8.34 1.01 6081 102 22-24 564 0.21 23 1.58 7.88 1.02 5115 89 24-26 591 0.24 25 1.81 7.81 1.02 4400 115 26-28 940 0.28 27 2.07 8.14 1.02 3665 222 28-30 644 0.32 29 2.36 7.60 1.02 2597 182 30-32 591 0.39 31 2.71 7.33 1.03 1847 197 32-34 483 0.48 33 3.14 6.91 1.04 1170 189 34-36 295 0.65 35 3.70 6.12 1.05 623 134 36-38 107 0.98 37 4.48 4.69 1.08 284 56 38-40 81 2.11 39 5.81 3.64 1.17 146 48 Total 7,410 59,304 1,517 East Afr. J. Biophys. Comput. Sci. (2023), Vol. 4, No. 1, 28-41 40 CONCLUSIONS & RECOMMENDATIONS The growth pattern of C. carpio was negative allometric which implied that the fish became thinner as its body length increases. The wellbeing of the fish was not in a good health condition. The mean length groups ranged from 17 cm to 33 cm was about 87% of the total catch and had a high contribution in fish yield. The C. carpio in Ayalew reservoir can grow up to the maximum length (L∞) 41 cm with growing speed (k) of 0.52 per year. The long lifespan (A0.95) of the cohort and the length at first maturity were 5.5 years and 23.44 cm, respectively. The production potential of the fish was about 7.4 tons of fish biomass and 1.5 tons of yields per year. However, investigation on reproductive biology, limnological aspects and stock enhancement are required in the reservoir. 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