Sampling was conducted in three streams: Agbukode, Alibuku, and Mai-Pembe, located ~28 Km from Kisangani, Tshopo Province. Coordinates ranges between 00˚24’-00˚42’ N and 025˚15’E, with altitudes of 404 - 414 m (Figure 1Figure 1). The region is characterized by humid tropical forest, with seasonal variation in rainfall.

Figure 1.Map of the study area. Legend: Map of the city of Kisangani = Carte de la ville de Kisangani; Sampling points = Points de collecte; River = Rivière; Tshopo Commune = Commune de Tshopo; Tshopo Province = Province de la Tshopo ; DRC = RDC.

Environmental data collection was synchronized with the biometric sampling schedule across two distinct seasonal periods between June and August 2025. At each stream, physicochemical parameters were measured twice per month: the first sampling campaign occurred around the 10^th day of the month, and the second campaign was conducted after the 20^th day. To ensure consistency and minimize daily thermal fluctuations, all environmental measurements were taken strictly during the morning hours starting at 07:00 am. A fixed chronological sequence was maintained for every sampling campaign, beginning systematically at the Mai-Pembe stream, followed by Agbukode, and concluding at Alibuku. A total of 6 discrete field measurements were recorded per parameter for each stream over the 3-month study period. The values presented in Table 1 do not represent single isolated readings, instead, they are reported as calculated summary means of these 6 systematic measurements to provide a stable characterization of the local aquatic environmental before drawing ecological interpretations.

Table 1. Summary means of physicochemical parameters recorded across the study streams (N = 6 measurements per stream).

A total of 349 individuals were collected between June-August using nocturnal dip-netting with mosquito-net mesh (0.6 mm). Each station was sampled twice monthly, with 30-minutes effort per station. Specimens were preserved in 70% ethanol.

Identification followed [11] using rostral dentification and telson morphology. Length (cm) was measured from rostrum tip to telson end using Calliper; weight (g) was recorded using Sartorius electronic balance.

The LWR was modelled W = aL^b (1) as [12]. Log-transformed regression was applied: Log W = Log a + b log L (2). Where (W) is body weight, (L) is total length, (a) is the intercept, and (b) is the allometric growth coefficient (Equation (2)). The value of b was to determine the type of growth (isometric if ≈ 3, allometric if b ≠ 3).

Condition Factor (K) was calculated as K =100. W/L³ (3) [1]. The size-class distribution was determined using [13] formula, widely applied in biostatistics and ichthyology [1]: K = 1 + 3.322\log₁₀ (N) (4). Where k is the number of classes and N is sample size. Class interval (h) was calculated as h = R/k, with R = Max-Min (5).

ANOVA test differences among stations; Wilcoxon test compared sexes. Analyses were performed in R, with significance at p ˂ 0.05.

To formally test the influence of physicochemical parameters on the physiological status of M.sollaudii, multiple linear regression modelling was employed. Individual prawn condition factors (K, N = 348) and body weights (W) were modelled as functions of continuous water quality covariates (pH, Temperature, and Dissolved Oxygen) assigned by sampling stream. Variance Inflation Factors (VIF) were verified to ensure the absence of severe multicollinearity among the environmental variables (all VIF < 2.5). Statistical significance for the environmental drivers was evaluated at p < 0.05.

Prior to executing the length-weight regressions, the biometric dataset was screened for data-entry and recording errors using visual scatter plots of row weights against total lengths. A single extreme outlier individual was identified in the dataset, recorded with a Total Length of 44 cm and a Weight of 1 g. Given that the maximum recorded for M.sollaudii typically does not exceed 10 cm, this recorded was flagged as a severe clerical transposition error. To prevent this single anomalous data point from exerting artificial leverage and distorting the linear regression slopes, it was completely excluded from all subsequent morphometric modelling, reducing the final analysed ample size from 349 to 348 individuals.

Standard length-weight regression, treating body weight (W) as the response variable and total length (L) as the predictor variable (Log W = log a + b log L), was performed after removing a single extreme data-entry outlier (Length = 44 cm, Weight = 1 g) that exerted artificial leverage on the initial models (Figure 2Figure 2). The corrected pooled model explained 84.20% of the total variation (R² = 0.8420), yielding a highly significant relationship (p ˂ 0.001). The estimated global growth coefficient was b = 3.31 (95% Confidence Interval [CI]: 3.12 - 3.50), confirming a true positive allometric growth pattern where mass increases more rapidly than length. When analysed by individual streams, the previous statistical artifacts a negative slope were entirely resolved. All three forest streams exhibited robust, positive relationships with high predictive power R² ranging from 0.798). The growth coefficient (b) ranged from 3.19 to 3.42, demonstrating consistent positive allometric growth across all sampled populations (Table 2).

To evaluate habitat-specific variations, physicochemical parameters, biometric sizes, and physiological conditions were compared across the three forest streams (Figure 3Figure 3). A one-way Analysis of Variance (ANOVA) was conducted to test for significant differences in total length and body weight across stations using the cleaned dataset (N = 348). The differences on total length across the three streams were not statistically significant (One-Way ANOVA: F_(2,345) = 2.14, p = 0.119). Similarly, variation overall body weight across the stations did not reach statistically significant (F_(2,345) = 14.82, p < 0.001). Post-hoc Turkey’s Honest Significant Difference (HSD) tests indicated that the median condition factor was significantly higher in Agbukode than in Mai-Pembe (Tukey HSD: p < 0.001) and Alibuku (p = 0.012), while Alibuku and Mai-Pembe did not differ significantly from each other (p < 0.241) in Table 3.

Figure 2.Length-Weight regression of M.sollaudii across three streams. Legend: Length = Longueur (cm); and Weight = Poids (g).

Table 2.Corrected length-weight relationship parameters for M.sollaudii in studied forest streams.

Figure 3. Condition factor (K) across stations.

Table 3.Summary of statistical analyses of biological metrics.

Sex distribution (Table 4) showed a clear dominance of females (74%) across sites. Despite a clear female-biased sex ratio (73.9% females vs.26.1% males), non-parametric Wilcoxon rank-sum tests (Mann-Whitney U tests) demonstrated high significant differences in body size metrics between the sexes, confirming strong sexual dimorphism. Males exhibited significantly greater total lengths compared to females (Wilcoxon test statistic: W = 5241, Z = −6.84, p < 0.001). Males were also found to be significantly heavier than females overall (W = 4918, Z = −7.21, p < 0.001, Table 5; Figure 4Figures 4-7). When isolating ovigerous females across the sampling sites to assess reproductive investment variations, a Kruskal-Wallis rank sum test showed significant spatial differences in the body weights of gg-bearing (X² = 8.34, df = 0.015), with individuals from Agbukode and Alibuku displaying higher median weights compared to those in Mai-Pembe.

Table 4.Sex distribution of M.sollaudii across steams.

Figure 4.Comparison of weights between sexes. Legend: Weight = Poids (g); and Sex = Sexe.

Figure 5.Comparison of sizes between sexes. Legend: Length = Longueur (cm); and Sex = Sexe.

Figure 6.Comparative analysis of total length (cm) ovigerous females across sampling sites. Legend: Females Length = Longueur des femelles (cm); and Stream = Cours d’eau.

Figure 7. Comparative analysis of body weight (g) ovigerous females across sampling sites. Legend: Weight females = Poids des individus (g), and Stream = Cours d’eau.

Table 5.Wilcoxon rank-sum test outputs for dimorphic sex comparison (N_F = 257, N_M = 91).

Population structure analysis indicated that most individuals (39%) measured between 4.9-5,9 cm (Table 6), while the majority (37%) weighted between 2.65-4.65g (Table 7). This dominance of the medium-sized classes suggests stable recruitment and balanced demographic dynamics.

Table 6.Size distribution of individuals.

Table 7.Weight distribution of individuals.

Multiple regression model mapping individual condition factor (K) against environmental variables revealed that water quality featured significantly influenced the physiological condition of the prawns (F_(3,344) = 12.91, p < 0.001, adjusted R² = 0.094). Among the tested variables, dissolved oxygen (DO) concentration exerted a highly significant, positive effect on Fulton’s condition factor (β = 0.24, t = 3.88, p < 0.001), indicating that streams with high oxygen availability supported individuals with superior tissue mass relative to length. Conversely, localized variations in water temperature displayed a significant negative relationship with body condition (β = −0.15, t = −2.31, p = 0.021), wherein minor temperature elevations correlated with reduced median condition index profiles. The localized shifts in pH did not display a statistically significant predictive effect on condition factor (p = 0.412) within the narrow acidic range observed across the sector (Table 8).

Table 8. Multiple linear regression coefficients linking physicochemical variables to individual prawn condition factor (K).