The newly created reservoir of Samandeni is located in the Volta catchment of Burkina Faso between latitudes 11˚23' and 11˚19' North and longitudes 4˚34' and 4˚46' West (Figure 1). This waterbody got the status of Ramsar site in October 2020. It has 153 km² wide with a storing water capacity of more than one billion of m³. The fish specimens were collected from the catch of local fishers on a monthly basis from March 2021 to February 2022 by considering nine sampling sites as shown in Figure 1. Fish species were identified by using the standard identification key given by [9] . In the Samandeni reservoir, the fishermen are known to use a variety of fishing methods such as gillnets with a mesh size of 10 to 40 mm, longlines, traps and cast net.

Fish were randomly sampled from several fishermen to ensure a good representation of classes size of the samples. Each specimen was examined on the same day they were collected to avoid error in the morphometric measurements posed by fixation. The total length (TL in cm) of each fish was measured by using empirical approach to calculate length frequency (LF) distributions according to [10] and to estimate growth parameters. The body weight (W) of each specimen was also measured (0.1 g) using an electronic balance.

A monthly collected value of TL, grouped into class intervals of 2 cm according to [10] empirical approach, was used to estimate the fish population demographic parameters. The asymptotic length (L_inf), the coefficient of growth (K), the amplitude of the growth oscillations (C), and the fraction of a year from which the sine wave oscillation begins (t_s) were estimated using TropFishR, an R package ver. 3.5.1 for tropical fisheries analysis [11] . Since we collected length-frequency (LF) data, the electronic length frequency analysis method (ELEFAN) was employed to estimate the population demographic parameters [12] . A bootstrapped ELEFAN with genetic algorithm optimization function developed by [13] was applied to analyze LF data, allowing the assessment of the uncertainties around the growth parameters estimate. Growth was modeled based on Von Bertalanffy growth function (VBGF) and a seasonally oscillating VBGF was used to assess the growth parameters, which are attributed to changes in water temperature, precipitation and to the availability of food [14] . The seasonally oscillating VBGF [15] used is given as:

L t = L i n f ( 1 − e − ( k ( t − t 0 ) + S ( t ) − S ( t 0 ) ) ) (1)

With S ( t ) = ( C K / 2 π ) sin 2 π ( t − t s ) , S ( t 0 ) = ( C K / 2 π ) sin 2 π ( t 0 − t s ) , and L_t is the length -at -age t, L_inf is the asymptotic length, K is the von Bertalanffy growth constant, and t₀ is the theoretical age when length equals zero, C is a constant indicating the amplitude of the oscillation, typically ranging from 0 to 1, and t_s is the fraction of a year where the sine wave oscillation begins (turns positive). A value of C = 1, growth stops completely once a year at a point in the annual cycle while values of C ranging between 0 and 1 results in slowed but not stopped, growth [16] .

Total mortality coefficient (Z) is estimated using the length-converted catch curve method [17] from the pooled length frequency data for the study period. The slope (b) of the curve with sign change gives the total mortality. The natural mortality rate (M) was calculated using [18] equation as follows:

M = 4.118 K 0.73 L i n f 0.33 (2)

Fishing mortality coefficient (F) was estimated by subtracting the value of natural mortality coefficient (M) from the value of total mortality coefficient (Z) as follow:

F = Z − M (3)

The body length at first capture (L_c) is the average selection length and was estimated by interpolation of the cumulative catch curve. The probability of capture for each length class was obtained from the backward extrapolation of the linearized capture curve, according to [17] . The extrapolated points from the length-converted catch curve were used to estimate the catch probability using the moving average method to estimate the length at first capture (L_c) by linear interpolation.

The capture probability was estimated by backward extrapolation, of the descending limb of the length-converted catch curve. A selectivity curve was generated using linear regression fitted to the ascending data points from a plot of the probability of capture against length, which was used to derive values of the lengths at capture at probabilities at 50%, 75% and 95% [12] .

The body length at which 50% of the individuals were mature (L_m) was estimated by fitting the logistic function of a non-linear regression relating the proportions of mature individuals (P) and total length (LT) of fish. The gonads maturity stages were determined by macroscopic observation of gonads according to [19] . Specimens falling within Stage II and above gonad maturity were used for L_m determination. The percentage of mature individuals in each length class was calculated based on the total number of individuals in each length class. The sigmoid curve was fitted using a standard logistic regression model as follows:

P = 1 1 + exp − ( α L T + β ) (4)

where α and β are the model parameters. Following a logarithmic transformation of the previous formula and, by substituting P = 50% in the equation, L_m is obtained by:

L m = − α β (5)

The stock status was estimated using the LBB method that is a recent approach developed by [20] to access the stock, which uses LF data as input and applies a Bayesian Monte Carlo Markov Chain (MCMC) method to estimate indicators of stock status. The LBB model is suitable for species that grow throughout their life, such as most economic fish and invertebrates [20] . It estimates the asymptotic body length (L_inf), the first catch body length (L_c), the relative natural mortality (M/K) and the relative fishing mortality (F/M) in the age range represented by LF samples. With these parameters as input, standard fisheries equation can be used to estimate the depletion or the current exploited biomass with regards to unexploited biomass (B/B₀). These parameters also allow the estimation of the length at first capture that would maximize catch and biomass for the given fishing effort (L_{c_opt}), and the estimation of a proxy for the relative biomass capable of producing maximum sustainable yields (B_MSY/B₀). The method uses a pre-specified set of priors, but users can manually incorporate priors for asymptotic length (L_inf), length at first capture (L_c), and relative natural mortality (M/K) from local studies. The L_inf priors were estimated from the median maximum length rather than the absolute maximum length. The M/K prior is set as 1.5, and L_c is estimated from the catch curve generated from the input LF data. We used a first maturity body length (L_m) estimates by this study and computed the numeric percentage of specimen in the catches larger than L_m. In the present study the basic equations are given below, however, for more details on the LBB method see [20] . Table 1 stands for the basic information and priors (L_inf, L_c, Z/K, M/K, F/K, and α) for the LBB analysis.

The analysis presented was done using R code (LBB_33a. R), downloaded from http://oceanrep.geomar.de/44832/ following the guidelines provided in the new user guide of the code.

First of all, a major assumption of the LBB method is that growth in length follows VBGF:

L t = L i n f [ 1 − e − K ( t − t 0 ) ] (6)

where L_t is the length at age t, L_inf is the asymptotic length, K is the growth rate, t₀ is theorical age at length null.

The function of the selectivity ogive (here assumed trawl like) is given by the following equation:

S L = 1 1 + e − α ( L − L c ) (7)

where S_L is the fraction of individuals that are retained by the gear at length L, and α describes the steepness of the ogive and L_c the body length at first capture.

To estimate L_inf, L_c, α, M/K and F/K simultaneously the following equation fitting the whole fishing quantity curve are use:

N L i = N L i − 1 ( L i n f − L i L i n f − L i − 1 ) M K + F K S L i (8)

C L i = N L i S L i (9)

where L_i is the number of individuals at length i, L_i−1 is the number at the previous length, where C refers to the number of individuals vulnerable to the gear and all other parameters are as described above.

A total of 2844 fish specimen were sampled from March 2021 to February 2022. As shown in Figure 2, the length distribution was unimodal, and ranged from

7.8 to 27.4 cm. Analysis shows that the mean and median are practically confused for male and female, indicating that the length frequencies are normally distributed in these samples. The mean length is 16.3 and 16.9 cm respectively for male and female, showing that male is longer than female on average.

The parameters of growth as estimated from the maximum density shown in Table 2 and illustrated by Figure 3 and Figure 4. The growth rate (K) was 0.94 yrs⁻¹ for male fish while that of females were 0.68 yrs⁻¹. The asymptotic length (L_inf) was 29.4 and 27.4 cm respectively for males and females. Figure 4 shows the reconstructed length distribution frequency diagram of the assessed fish species. The value of growth oscillation intensity (C) suggests seasonality in the growth of the Mango tilapia and the summer point (t_s) was estimated as 0.58 (July) and 0.41 (June) as shown in Figure 3. The estimated K values were similar between male and female fish: 0.72 and 0.61/yrs for female and male respectively (Figure 4).

The maximum density estimates of the parameter t anchor was 0.57, and 0.64, representing the months of September, where yearly repeating growth curves cross length equal to zero for female and male of S. galilaeus respectively.

The total rates of mortality (Z) obtained through the length-converted catch curves were 2.64 yrs⁻¹. The parameters of natural mortality rate (M) estimated by Then (2015) empirical equation were 1.33 yrs⁻¹. For Samandeni reservoir, the fishing mortality rates (F) were 1.27 yrs⁻¹.

The length at first capture (L_c) was 17 cm. Figure 5 shows the probability of capture in terms of age.

The body length at first sexual maturity of the Mango tilapia, S. galilaeus was 12.5, 12.1 and 13.4 cm for both sexes, male and female, respectively (Figure 6). The least matured male and female in the population were 8.3 and 7.8 cm TL, respectively. The difference is not statistically significant although females appear to reach sexual maturity early.

Results for specific evaluation of LBB method are shown in Figure 7. The blue line in the figure represents the fit of the data and the LBB estimation, and the red line is the evaluation from the LBB method of population resources. The parameters L_mean/L_opt, L_c/L_{c_opt}, L_95th/L_inf, B/B₀, B/B_MSY, B_MSY/B₀ and F/M were obtained (Table 3). The estimated B/B_MSY was 1.2, which confirmed the healthy condition of S. galilaeus stock and indicated the safe biomass level capable of producing the MSY. The calculated B/B₀ (0.41) was near to the reference limit for S galilaeus stock biomass and denoted that the biomass was in the safe condition, whereby 59% of the wild stock of this species were harvested. The assessed B/B₀ was larger than B_MSY/B₀ (0.34), and F/M was 0.81, which indicated an underfishing condition. The output result for L_c/L_{c_opt} and L_mean/L_opt was 1, showing the healthy stock with the fishing of prominent individuals. The output result of L_95th/L_inf (=0.91) is close to one, indicating the presence of the significant number of individuals in the stock. In addition, a smaller estimation was observed for L_c (17) than L_{c_opt} (20), indicating that overfishing increased and recommended that the first fish catch was more extensive. Furthermore, the B/B₀ > B_MSY/B₀ and L_c < L_{c_opt} suggested that the existing fishing pressure and mesh sizes may influence the biomass. However, it is recommended that slightly increasing the mesh size for fishing may be beneficial for the sustainability of the S. galilaeus population in the Samandeni.