Probiotic-enriched black soldier fly larvae meal diets impact the relationship between morphometric traits and the growth of Redbreasted tilapia (Coptodon rendalli)

Probiotic-enriched black soldier fly larvae meal diets impact the relationship between morphometric traits and the growth of Redbreasted tilapia (Coptodon rendalli) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Probiotic-enriched black soldier fly larvae meal diets impact the relationship between morphometric traits and the growth of Redbreasted tilapia (Coptodon rendalli) Thaddeus Zaabwe, Elias Chirwa, Kumbukani Mzengereza, Jeremiah Kang’ombe, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9672785/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Fish morphometry (body shape) is a key variable that determines a fish’s ability to feed, swim, reproduce, and perform other biological processes. Morphometric traits indicate fish health and overall well-being, yet their use in determining aquaculture production when probiotics are used in insect diets is not well understood. The relationships of fish morphometric traits with growth parameters have also not been well investigated. The current study describes the performance and relationship of body morphological variations and specific growth trajectories of Redbreasted tilapia fingerlings fed commercial probiotic-inoculated black soldier fly meal diets. The fish were reared for 72 days and fed various probiotic-inoculated diets described as P0 [0 CFU/kg] as a control , P1 [4×10¹⁰ CFU/kg] as treatment 1, P2 [8×10¹⁰ CFU/kg] as treatment 2, and P3 [12×10¹⁰ CFU/kg] as treatment 3. Significantly greater means for the morphometric traits of total length, eye diameter, distance between eyes, operculum length, and pectoral fin length were observed at T2 (p < 0.05). Similarly, the specific growth rate and weight gain were greater in T2, with T0 indicating the highest apparent feed conversion ratio (p < 0.05). Overall, the T2 inoculation level presented the greatest variability, with enhanced morphology-related growth in the present study. The study indicated that probiotic inoculation in BSFL diets enhances morphological variability and fish growth. body shape morphometry inoculation geometric analysis landmark truss network Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Morphometric analysis, which is the quantitative analysis of body shape, length, and weight, forms the core of aquaculture and fisheries science, as it reveals the growth status, health, and ecological adaptation of fish populations (Kenneth et al. 2024 ). This involves the use of ecological informatics by providing precise tools for shape analysis, especially in fish (Talijan 2026 ). Fish deformities and body shape are important qualities for fish selection in aquaculture production; however, they are usually secondary to other growth parameters (Sutthakiet et al. 2020 ). Moreover, morphometric traits offer evidence of how well habitats support the functions of aquatic organisms (Hussein et al. 2025 ). In aquaculture fish selection programs, a trend has evolved whereby these traits are incorporated to predict fish performance. Morphometric traits significantly affect the preferences of consumers and their willingness to pay for fish (Mehar 2019 ). The most commonly used morphometrics include the length‒weight relationship and condition indices to provide inferences on body condition, energy stores, physiological fitness, and quality of growth of cultured and wild fish stocks (Brosset et al. 2023 ). Morphometric measures are important in that they combine variables of size, shape, and energy reserves, thereby providing estimations of nutritional status, health, and biomass productivity that are often more sensitive than simple measures of body weight alone (Traverso et al. 2024 ). Additionally, morphometric traits are typically pseudo representations of fish condition, health, and biomass productivity, and they yield more information than just length or weight. They have positive impacts on growth performance, feed utilization, antioxidant status, immune functionality, and the gut microbiota in a number of cultured fish species, such as tilapia, seabass, and catfish (Wang et al. 2025 ). The use of geometric morphometric methods enables solid visualization and measurement of morphological changes by separating the shape and non-shape parts and the ability to produce biologically significant morphological changes (Coure et al. 2025 ). In some cases, fish morphology has been used to determine fish escape from culture systems such as cages (Terje et al. 2026 ). Nonetheless, morphometric responses, including shifts in shape or condition indices compared with growth performance, are still poorly investigated. For example, while recent work on Atlantic salmon feeding trials explored shape variation under divergent protein sources, the short time frames, limited sample sizes, and limited definitive conclusions about diet-induced morphometric responses (Chollet-Villalpando et al. 2025 ). Although fish growth parameters such as the apparent feed conversion ratio (AFCR), protein efficiency ratio (PER), specific growth rate (SGR), weight gain, and relative condition factor ( Kn ) have been extensively used over time, a significant gap in the research concerning their combination with deep morphometric responses when probiotics and insect meals are used in the world of aquaculture nutrition. These measures quantify gross changes in mass and feed efficiency but do not measure indirect morphological changes in body shape or structure, which can help to identify other effects of dietary treatments on fish physiology (Pamphile et al. 2024). This is further overstated in research that involves the use of insect meals to replace fish meal as a substitute ingredient and in probiotic-inoculated diets (Torres-Maravilla et al. 2024). Additionally, the effects of insect-based diets on growth performance and health parameters in different fish species have been demonstrated. However, limited emphasis has been given to morphological changes in fish. Insects such as black soldier fly (BSF) larvae, when used in aquaculture, improve their histology, reduce enterocyte steatosis in the pyloric caeca, and reduce IgM levels in the distal intestine of Atlantic salmon ( Salmo salar ) (Weththasinghe et al. 2021 ). Similarly, BSFL diets improve lipid deposition and fish welfare in gilt heads (Randazzo et al. 2021 ) and rainbow trout ( Oncorhynchus mykiss ) (Melenchón et al. 2022 ). Probiotics contribute to good fish performance, but this has been limited to putative (naturally isolated) probiotics. Fish rely on the microbiota to digest non-starch polysaccharides such as chitin (Egerton et al. 2018 ), and their digestion is greatly enhanced when probiotics are utilized (Hasan et al. 2023 ). Our present study examined the effects of exogenous probiotic inoculation of black soldier fly diets on morphometric responses, specific growth parameters, and their relationships with Redbreasted tilapia reared in concrete fish tanks. The findings will generate valuable scientific evidence, which will guide the imminent rearing of Redbreasted tilapia ( Coptodon rendalli ) and other cichlid species. Methods Study area, fish husbandry, and sampling The study was carried out at the Domasi Aquaculture Centre in Zomba District, southern Malawi, which is located at coordinates of 11° 46' 703” S, 34° 13' 082” E. Fish husbandry In total, 480 Redbreasted tilapia fingerlings weighing 4.1 ± 0.10 grams were sourced from the Domasi Aquaculture Centre hatchery, Zomba district, Southern Malawi, and and used in the experiment. The fish were harvested and conditioned for 1 week in 2 m³ concrete fish tanks and fed a 40% crude protein exogenous diet. Probiotic feed preparation Exogenous Sanolife® PRO-2 probiotic mixtures ( Bacillus subtilis, Bacillus licheniformis, and Bacillus pumilis obtained from INVE Aquaculture, Belgium) were used at T0 [0 CFU/kg] as a control, T1 [4×10 10 CFU/kg] as treatment 1, T2 [8×10 10 CFU/kg] as treatment 2, and T3 [12×10 10 CFU/kg] as treatment 3. The probiotic concentrations were homogenized in 2 litres of fish oil. The formulated feed pellets were top-dressed evenly with the prepared mixture. The pellets were left to absorb the probiotic mixture for a period of 30 minutes to 2 hours at room temperature (25°C). Top dressing was applied at intervals of 3 days for the entire culture period, as this was the ideal maximum time at which the probiotics would remain attached to the pellets. Diet formulation A 42% crude protein isonitrogenous and isocaloric BSFL-based probiotic-inoculated fish diet was formulated using locally available ingredients sourced from Mzuzu market, Mzuzu City, northern Malawi. BSFL was sourced from the BSF culture unit at the Department of Fisheries and Aquatic Sciences at Mzuzu University, Malawi. Prior to feed formulation, the BSFL and sourced ingredients were subjected to proximate analysis for crude protein, crude lipid, crude fibre, nitrogen-free extract, and ash contents while adopting the standard methods of AOAC (2000). The crude protein content was determined via the Kjeldahl system (Kjeltec™ 8400-Autoanalyzer, FOSS, Denmark), the crude lipid content was determined by the Soxhlet method, and the crude ash content was determined through combustion in a muffle oven, which involves oven incineration at 550°C for 5 hours. The moisture content was also determined through oven drying at 105°C. The pulverized ingredients were homogenized using a clean spade and extruded at approximately 90–120°C \(\:\:\) using an electric-powered pelletizer (Model: Y2-160 M-4, Dongxiang Hongxiang, China) to form 1 mm feed pellets. The formulated pellets were spread on clean synthetic tarpaulin and dried under shade for four days to reduce moisture. The inclusion levels of the formulated diet are indicated in Table 1 . The formulated BSFL diet was subjected to proximate analysis before its application in the experiment. This was done to control for any quality checks in the feed, according to the dietary nutrient requirements of the fish. The feed was processed and stored in a well-aerated room at 25°C. Table 1 Ingredient inclusion levels for the 42% CP probiotic inoculated BSFL fish feed. Ingredient Inclusion level (%) Defatted black soldier fly larval meal 44 Deoiled soybean meal 20 Maize 5.8 Wheat flour 15 Wheat bran 12 Vitamin premix 1 Monocalcium phosphate 1 Stabilized Vitamin C 0.1 Salt 1 Chitinase enzyme 0.1 Total 100 Proximate composition (%) Crude protein 42.44 Crude lipid 8.24 Crude fibre 2.94 Digestible Energy (Kcal/kg) 4082.94 Premix analysis per 2000 g; vitamin An 11,500 IU, vitamin D 3 3,000 IU, vitamin E 10,000 IU, vitamin K 2,000 mg, vitamin B 1 1,000 mg, vitamin B 2 4,000 mg, vitamin B6 500 mg, vitamin B12 6000 mcg, choline 150000 mg, iron 30000 mg, manganese 80000 mg, copper 5000 mg, zinc 50000 mg, cobalt 225 mg, iodine 1000 mg, selenium 150 mg, lysine 20000 mg, methionine 25000 mg, phosphorus 5250 mg and calcium 750 mg, calcium: 2.057%, phosphorus: 1.08%. Experimental setup Each concrete tank measuring 2×1×1 m was stocked with 40 fingerlings during the onset of the experiment. Probiotic inoculation levels were determined in triplicate ( n = 3 ). The fish were fed ad libitum throughout the experiment. When feed utilization was not efficient, adjustments were made in relation to the proportionate average body weights. The experiment was carried out in a completely randomized design for 72 days. To maintain a clean environment, the fish tanks were routinely cleaned biweekly to eliminate any fecal matter and uneaten feed. Fish sample collection Body condition indices The relative condition factor ( Kn ) was analysed using Le Cren's (1951) formula: Kn = W \(\:∕\) aL b The specific growth rate (% \(\:∕\) day) was calculated via the following formula: SGR (% \(\:∕\) day) = Ln final weight – Ln initial weight \(\:∕\) time (days) × 100 Weight gain was analysed via the formula described by Silva & Davy ( 1992 ). Weight gain (g) = Wt 2 – Wt 1 Image acquisition and shape analysis At the end of the experiment, a total of 30 fish fingerlings per inoculation level were harvested from the fish tanks and placed in 10-liter water-filled clean buckets. To minimize fish stress, the fish were immobilized in 40 mg/L dissolved clove solution following the protocols used by Anderson & McKinley ( 1997 ). This was conducted until stage 4 of anaesthesia was achieved (rare opercular movements, relaxed muscle tone, and no reaction to stimuli). The Truss Network system standard protocol was used to analyse body morphometrics (Strauss & Bookstein 1982 ). The method is non-destructive and well refined for any morphological variation across fish of various subpopulations (Ines et al., 2024 ). It further helps in the estimation of, and compensation for, measurement error. The homologous landmarks are defined along the contours of the fish body while the distances between them are quantified. Each fish was laid on its left side against a white background, the fins were stretched out, and the fish were photographed from a fixed position of 40 mm at an elevated angle of 90°. The images were captured using a Canon DS126761 high-resolution digital camera (Canon Zoom Lens EF-S 18–55 mm, φ58 mm; Canon Inc. 3–30–2, Shinomaruko, Ohta-ku, Tokyo, Japan). In total, 20 anatomical landmarks were identified, which defined 20 distances as reflected in Fig. 1 . The captured images per inoculation level were imported into TpsUtil software (v1.78) to generate an image file. The generated image files were imported into TpsDig software (v2.31) for landmarking (digitization). These software programs help in the actual placement and recording of X and Y landmarks, thus enabling the construction of a truss network. The morphometric traits, landmarks, their numbers, and locations are described in Table 2 . The upper part of the table describes the landmark, the morphometric trait, and its abbreviation, whereas the lower part describes the number and location. Table 2 Morphometric distances and landmarks described along the body contour of Redbreasted tilapia used for body shape analysis. Please refer to Fig. 1 as well. Body landmarks Landmark Morphometric trait Abbreviation 1 to 2 Head Length (cm) HL 1 to 6 Total Length (cm) TL 2 to 3 Dorsal Fin Length (cm) DSFL 5 to 6 Caudal Fin Length (cm) CFL 8 to 9 Anal Fin Length (cm) AFL 10 to 11 Pelvic Fin Length (cm) PVFL 12 to 13 Operculum Length (cm) OL 14 to 15 Distance Between Eyes (cm) DBE 16 to 17 Eye Diameter (cm) ED 18 to 19 Pectoral Fin Length (cm) PCFL 2 to 20 Body Depth (cm) BD Number Location 1 Anterior tip of the fish snout 2 Anterior point of the dorsal fin 3 Posterior tip of the dorsal fin 4 Terminal part of the dorsal fin 5 Anterior part of the caudal fin 6 Posterior tip of the caudal fin 7 Posterior end of anal fin 8 Posterior tip of the anal fin 9 Anterior part of the anal fin 10 Posterior tip of the pelvic fin 11 Anterior part of the pelvic fin 12 Lower part of the operculum 13 Upper part of the operculum 14 Anterior part of the distance between eyes 15 Posterior part of the distance between eyes 16 Anterior point of eye diameter 17 Posterior point of eye diameter 18 Anterior part of the pectoral fin 19 Posterior part of the pectoral fin 20 Point of body depth measurement The body shape variation across PC1 covariance matrix scores (Fig. 2 ) was used to generate a wireframe (shape variation) diagram (Fig. 3 ) with demarcated distances. This was carried out using the generalized Procrustes analysis (GPA) for all landmarks under each section, which were pooled together via MorphoJ v1.70a software. To minimize the binary cross-entropy between the predicted heatmap h k and the ground truth heatmap \(\:\widehat{h}\) k for each landmark k , the loss function was formalized via the following equation: $$\:L=\frac{1}{KHW}\sum\:_{k}\sum\:_{xy}-{h}_{xy}^{x}.log{\widehat{h}}_{xy}^{k}-\left(1-{h}_{xy}^{k}\right).log\left(1-{\widehat{h}}_{xy}^{k}\right)$$ where k denotes one of the K landmarks and where x and y represent the height (H) × width (W) of the sized image. Statistical analysis Multivariate morphometric analysis and growth parameters A multivariate statistical approach of principal component analysis (PCA) was used to determine the physiological state of Redbreasted tilapia across inoculation levels and estimate the extensive relationships between all the morphometric traits and growth parameters. To maximize variance in loadings, orthogonal rotation was performed via varimax rotation (Cozzolino et al. 2019 ). Multiple variables were summarized using the shared correlation structure to derive principal components. Each variable was scaled and centered. To study the statistical effect of individual explanatory variables on the morphometric body condition index, initial tests for multicollinearity between morphometric traits relying on the variance inflation factor (VIF) with values < 5 were carried out (Zuur et al. 2007 ). A generalized linear modelling (GLM) approach with a Gaussian distribution and a log link function (Xiao et al., 2018 ) was used to estimate the distributions of growth parameters and morphometric traits across inoculation levels. The extracted growth parameters and morphometric traits were subjected to normality and homogeneity of variance checks using Shapiro‒Wilk’s test and Levene’s test, respectively. One-way analysis of variance (ANOVA) was conducted to test for statistically significant differences between inoculation levels for morphometric traits and selected growth variables. Tukey’s honestly significant difference (HSD) post hoc test was used to separate statistically significant differences across probiotic inoculation levels. Single-variable linear regression was conducted to determine the functional relationships between the extracted morphometric traits and growth parameters. Statistical significance was considered at α < 0.05. Statistical analyses were conducted in Python programming v3.12.2 (Rossum 2025 ) via the SciPy (Virtanen et al. 2020 ), Matplotlib (Hunter 2007 ), Scikit-learn (Pedregosa et al. 2011 ), and PtitPrince libraries. The linear regression and principal component analysis equation models are described in equations i and ii , respectively. $$\:\widehat{\gamma\:}={\beta\:}_{0}+{\beta\:}_{i}X$$ (i) where \(\:\widehat{\gamma\:}\) represents the observed values of the growth parameter, \(\:{\beta\:}_{0}\) represents the intercept, \(\:{\beta\:}_{i}\) represents the coefficient, and X represents all the observed values of the morphometric traits. $$\:{PC}_{k}={a}_{k1}{X}_{1}+{a}_{k2}{X}_{2}+{a}_{k3}{X}_{3}+\cdots\:\cdots\:\cdots\:+{a}_{kn}{X}_{n}$$ (ii) where \(\:{PC}_{k}\) represents the k th principal component, \(\:{X}_{1},\:{X}_{2},\:{X}_{3}\dots\:\dots\:\dots\:.{X}_{n}\) represents the morphometric traits or growth variables, \(\:{a}_{k1}\) , \(\:{a}_{k2},\) \(\:{a}_{k3}\) ......... \(\:{a}_{kn}\) represents the coefficients or loadings of morphometric traits and growth variables, and n represents the number of morphometric traits or growth variables. Results Growth parameters The PCA biplot scores revealed that morphometric traits and growth-related parameters were interstructured, with PC1 as the major factor, with an explanation of 51.54% (Fig. 4 ). This finding indicated that total growth performance was the most prevalent influence on phenotypic differentiation across treatments. PC2, which was associated with the AFCR and SGR, accounted for 23.90% of the total variance. The observed morphological divergence indicated an inclination toward greater inoculation, which was correlated with increased growth, indicating treatment-dependent morphological divergence. Weight gain, SGR, and the AFCR were retained after rotation. The ANOVA results for the retained growth parameters are shown in Fig. 5 . The T2 inoculation level had the highest mean values for SGR (3.10 ± 0.07) and weight gain (9.12 ± 0.21), with T0 having the lowest mean values of 1.92 ± 0.06 and 4.40 ± 0.14, respectively (B & C). This was indicative of increased fish growth when T2-inoculated feed was used to feed the fish. In contrast, T2 had the lowest mean AFCR (1.56 ± 0.03), with T0 having the highest AFCR (2.26 ± 0.05) (A). The lowest AFCR was associated with better feed utilization by the fish. Statistically significant differences across inoculation levels were shown for SGR (p < 0.001), weight gain (p < 0.001) and AFCR (p < 0.001). Morphometric traits The correlogram heatmap indicates that all morphometric variables were highly correlated, with the lowest correlation reflected between the operculum and distance between eyes, with a Pearson’s correlation coefficient (R) of 0.80 (Fig. 6 ). The majority of morphometric traits were strongly correlated with each other, indicating covariance with size in general. Intercorrelations between total length, operculum length, pelvic fin length, pectoral fin length, and head length presented the highest Pearson’s correlation coefficient, R = 0.99. The results from PCA indicated that the data were suitable for PCA reduction, with a Kaiser‒Meyer‒Olkin test value of 0.87 and a Bartlett’s test of sphericity, p < 0.001, indicating that the variables were related. The PCA biplot revealed a distinct separation of the effects of inoculation on morphometric traits (Fig. 7 ). PC1 and PC2 accounted for 93.87% and 3.47% of the total variation, respectively. PC1 was highly related to total length, weight gain, body depth, and fin dimensions. These findings indicated that overall growth-related morphometric traits were affected mainly by the inoculation of probiotics. PC2 represents the secondary shape variation. The variables that were extracted after PCA orthogonal rotation included operculum length, eye diameter, pectoral length, total length, and distance between eyes. The results for the extracted traits are shown in Table 3 . The highest mean values for TL, ED, DBE, OL, and PFL were recorded at the T2 inoculation level. Statistically significant differences in morphometric traits were observed across all inoculation levels (p < 0.001). Additionally, statistically significant polynomial contrasts were indicated across inoculation levels for all morphometric traits (p < 0.001). Table 3 Mean (± SE) values of morphometric traits across inoculation levels Inoculation level (cfu/kg) Polynomial contrasts Morphometric trait T0 T1 T2 T3 Linear Quadratic Cubic TL (mm) 61 ± 0.49 a 71 ± 0.00 b 92.30 ± 0.80 d 74.6 ± 0.16 c < 0.001 < 0.001 < 0.001 ED (mm) 4.21 ± 0.04 a 4.48 ± 0.02 b 6.20 ± 0.00 d 5.86 ± 0.02 c < 0.001 < 0.001 < 0.001 DBE (mm) 6.51 ± 0.16 a 8.93 ± 0.02 b 12.57 ± 0.11 d 9.2 ± 0.00 c < 0.001 < 0.001 < 0.001 OL (mm) 7.77 ± 0.12 a 8.59 ± 0.03 b 12.84 ± 0.02 d 12.53 ± 0.02 c < 0.001 < 0.001 < 0.001 PFL (mm) 6.67 ± 0.52 a 10.24 ± 0.06 b 21.97 ± 0.21 d 19.93 ± 0.03 c < 0.001 < 0.001 < 0.001 *Values in rows with different superscripts are statistically significant at p < 0.05 according to Tukey’s HSD post hoc test . NOTE: TL – Total Length, ED – Eye Diameter, DBE – Distance between Eyes, OL – Operculum Length, and PFL – Pectoral Fin Length Hierarchical clustering of the inoculation levels (T0, T1, T2, and T3) on the basis of the mean morphometric parameters (Euclidean distance and Ward linkage method) is depicted by the dendrogram in Fig. 8 . Two major clusters are reflected: Cluster 1 (T0 and T1) and Cluster 2 (T2 and T3). The clusters converge at a far greater Euclidean distance (~ 45), suggesting great overall morphometric dissimilarity between the low inoculation levels (T0 and T1) and the high inoculation levels (T2 and T3). T0 and T1 are relatively small distances from each other (~ 14), implying that the fish in T0 and T1 have morphometric profiles that can be considered relatively similar. T2 and T3 are concentrated at the intermediate level (~ 22), hence being similar to more pronounced morphometric reactions. The results indicate that the morphological response to inoculation with T2 was dose dependent, and the greatest increase in morphometric parameters was found for all the parameters. The dendrogram supports multivariate separation as well as robust inoculation-induced structural partitioning of morphometric traits at the level of inoculation. The regression results between the PCA-extracted growth parameters and morphometric traits are shown in Table 4 . Positive Pearson’s correlation (R) values were observed between growth parameters and morphometric traits. Similarly, positive coefficients ( b ) were observed between the growth parameters of weight gain and SGR across all the morphometric traits. Morphometric traits positively influenced weight gain and SGR. However, the AFCR negatively influenced morphometric traits, as the results indicated negative coefficients ( b ). Overall, the regression model was statistically significant (p < 0.05). Table 4 Linear regression of extracted growth parameters and extracted morphometric traits Morphometric trait vs Growth parameter R R 2 R 2 Adjusted a b p value TL (cm) vs WG (g) 0.38 0.14 0.12 -1.01 0.06 0.017 ED (cm) vs WG(g) 0.34 0.11 0.09 -0.22 0.71 0.034 DBE (cm) vs WG(g) 0.37 0.14 0.11 0.58 0.31 0.018 OL (cm) vs WG (g) 0.34 0.11 0.09 0.67 0.27 0.033 PFL (cm) vs WG (g) 0.34 0.12 0.09 2.07 0.09 0.032 TL (cm) vs SGR (%) 0.39 0.14 0.13 0.01 0.05 0.014 ED (cm) vs SGR (%) 0.36 0.13 0.12 0.60 0.68 0.023 DBE (cm) vs SGR (%) 0.38 0.14 0.12 1.50 0.28 0.016 OL (cm) vs SGR (%) 0.36 0.13 0.11 1.45 0.26 0.022 PFL (cm) vs SGR (%) 0.36 0.13 0.11 2.79 0.09 0.021 AFCR vs TL (cm) 0.34 0.11 0.09 86.04 -7.79 0.033 AFCR vs ED (cm) 0.38 0.14 0.12 6.14 -0.66 0.016 AFCR vs DBE (cm) 0.31 0.09 0.07 11.30 -1.38 0.050 AFCR vs OL (cm) 0.38 0.14 0.12 12.99 -1.74 0.017 AFCR vs PFL (cm) 0.37 0.14 0.11 21.70 -4.82 0.020 Discussion This study focused on testing the hypothesis that morphometric traits and growth parameters such as the SGR, AFCR, Kn , and weight gain of Redbreasted tilapia are improved when the fish are fed probiotic-inoculated black soldier fly meal diets. Most studies have focused on the utilization of energy-dense variables such as fatty acids, proteins, and hormones to determine morphometric trait performance in fish. Morphometric traits act as representations of the energy-dense performance of a fish. The results from our study revealed that the fish growth parameters were closely linked to the morphometric traits, although the AFCR negatively contributed to the performance of the morphometric traits. The high SGR, weight gain, and AFCR could be linked to the enhanced digestive efficiency as a result of probiotics producing extracellular enzymes such as proteases, cellulases, amylases, and lipases. The enzymes efficiently breakdown proteins such as chitin, carbohydrates, and lipids, leading to increased nutrient absorption and hence facilitating faster growth (Hasan et al. 2021 ). Similarly, during the stunting periods of Milkfish for compensatory cell growth, the high SGR and morphometric growth were similar to those reported in the present study (Lingam et al. 2019 ). This finding supports the assumption that nutrient deprivation in fish affects their morphological traits (Pavlov 2015 ). Moreover, the morphological traits and growth parameters of SGR and weight gain decreased with limited probiotic inoculation. Similarly, the use of probiotics in insect aquafeeds increased shrimp fish weight and survival, as reflected in this study (> 95%) (Toledo et al. 2019 ). Probiotics improve fish weight gain and feed utilization indices in addition to intestinal morphological modifications in fish, especially Nile tilapia (Tabassum et al. 2021 ), increasing protein intake and hence increasing SGR and weight gain, corroborating the findings of this study (Du et al. 2021 ). Similar to this work, the application of Tinebrio molitor in Nile tilapia fish diets, including Saccharomyces cerevisiae , did not significantly alter the SGR (Anany et al. 2025 ). The high SGR and overall weight and performance values may be related to the synthesis of growth-promoting substances such as bacteriocins and other vitamins, particularly vitamin B complex, which improve the fish gut microbiota and function as natural growth promoters. The use of bacteriocins (Pereira et al. 2022 ) and multivitamins (Asadollahi et al. 2025 ) as alternatives to antibiotics in aquaculture increases fish growth and overall fish morphometric traits, as seen in our study. In homogenous fish rearing environments, morphological variation decreases with somatic growth (Lorena et al. 2023). In contrast, our study revealed increased variability in the morphometry of fish with somatic growth. This could be attributed to the variation in probiotic inoculation levels, which improved nutrient availability, leading to increased growth of various morphometric traits. The addition of the appropriate amount of probiotics should improve fish growth and morphometric trait performance. The addition of undesirable probiotic inoculation levels in basal diets for fish growth can lead to low growth rates and yields coupled with low survival rates, as reflected in the rearing of Clarias gariepinus (Hadijah et al. 2024 ). In our study, although T3 had a greater quantity than did T2, it was outperformed by the inoculation level T2. These findings suggest that the dosage for effective performance in Redbreasted tilapia in BSFL fish diets is ideal at the T2 level. The higher growth rate and morphometric traits in the probiotic-fertigated diets could be linked to the high digestive enzyme activities resulting from secretion by the diverse microbiota in the fish gut. The source of a fish can affect overall morphological trait performance. Fish that are usually sourced from the wild for culture do not show variability in morphometric growth (Delfina 2017 ). The fish utilized in this study were on-farm hatchery-raised fish with indications of morphological trait changes. However, morphometric trait analysis integrates external and internal phenotypic signals that increase the reliability of the findings on fish performance (Kulzer et al. 2026 ), auditory mechanisms (Robins et al. 2025 ) and shell morphology in freshwater mussels (Fassatoui et al. 2024 ). Moreover, the use of various morphometric traits effectively predicts variables such as total length, as used in our study, and regression models increase the number of morphometric traits that predict fish total length (Mathias et al. 2026 ) and weight (Saleh et al. 2024 ; Wasso et al. 2025 ). Several authors acknowledge that morphometric traits such as body depth and eye size, as reflected in our study, indicate significant changes in fish morphological changes and can also predict fish growth parameters such as weight gain (Third & Parsons 2024 ). Although geometric morphology can help in assessing homogeneity in morphological features (Binashikhbubkr et al. 2024 ), heterogeneity in fish morphometric traits across probiotic inoculation levels is notable. While our study revealed significant changes in morphometric traits and growth, some studies have shown contrasting results (no significant differences) in morphometric trait changes and fish growth, as indicated by Long ( 2026 ). These differences could be attributed to the addition of probiotic inoculation to the feed used. Various morphometric traits are affected differently when fish are subjected to different rearing conditions and diets. For example, in our study, the extracted traits of operculum length, distance between eyes, eye diameter, and total length contrasted with body depth and snout length in a study conducted by Majeed et al. ( 2026 ). Conclusion The results of the present study provide evidence that the morphometric traits and selected growth parameters of Redbreatsed tilapia are influenced by the addition of probiotics to black soldier fly meal diets. This study highlights the novelty of the use of selected probiotics and black soldier flies and how they improve fish morphometric traits, growth, and their relationships. The study further revealed that morphometric and selected growth parameters improved with increased probiotic addition. This effect was most pronounced in the T2 probiotic inoculation treatment. This study revealed that morphometric traits and selected growth parameters were integrative and influenced when probiotics were added to black soldier fly diets. The use of morphometric variation to determine fish fitness and performance is recommended when commercial probiotics at the right inoculation levels are used. Declarations Funding This work was supported by the World Bank ACE II Additional Financing Project ID P176744 under the African Centre of Excellence for Neglected and Underutilized Biodiversity (ACE NUB) at Mzuzu University, Malawi. Competing Interests The authors have non-financial interests to disclose. Funding This research was funded by the World Bank ACE II Additional Financing Project ID P176744 under the African Centre of Excellence for Neglected and Underutilized Biodiversity (ACE NUB) at Mzuzu University, Malawi. Author Contribution T.Z. Conceptualized and conducted the research, performed data analysis and visualization, provided the software, and wrote the main manuscript text. E.C., K.M. and J.K. supervised, wrote and reviewed the manuscript. G.L.A., P.A., I.B., P.S.K and N.N. wrote the manuscript. All authors reviewed the manuscript. Acknowledgement The authors are indebted to INVE Aquaculture, Belgium, for providing the commercial probiotic strain mixture used in this study. Data Availability The data are available upon request. References Anany EM, Ibrahim MA, Abd IM, Razek E, Said E, Nabawy M, El, Amer AA, Zaineldin AI, Gewaily MS, Dawood MAO (2025) Combined effects of yellow mealworm ( Tenebrio molitor ) and Saccharomyces cerevisiae on the growth performance, feed utilization, intestinal health, and blood biomarkers of Nile tilapia ( Oreochromis niloticus ) fed fish meal – free diets. Prob Antimicro Prot 17:1387–1398. https://doi.org/10.1007/s12602-023-10199-8 Anderson WG, Mckinley RS (1997) The use of clove oil as an anaesthetic for rainbow trout and its effects on swimming performance. N Amer J Fish Mgt 17:1–9. https://doi.org/http://dx.doi.org/10.1577/1548-8675 AOAC International (2000) Official methods of analysis of the AOAC International (17th ed) Asadollahi M, Baserh J, Abnaroodhelleh F, Kordyani MB, Samani MN, Dadar M (2025) Combined prebiotic and multivitamin supplementation enhances growth, survival, and disease resistance of Asian seabass in floating cages. Aqua Rep 43:1–8. https://doi.org/10.1016/j.aqrep.2025.102919 Binashikhbubkr K, Babangida J, Al-misned F, Naim D (2024) Stock structure delineation of Kawakawa Euthynnus affinis (Cantor, 1849) from Malaysian Borneo using multivariate morphometric analysis. J King Saud Uni - Sci 36:1–7. https://doi.org/10.1016/j.jksus.2024.103278 Brosset P, Averty A, Mathieu-resuge M, Schull Q, Soudant P, Lebigre C (2023) Fish morphometric body condition indices reflect energy reserves but other physiological processes matter. Ecol Ind 154:1–9. https://doi.org/10.1016/j.ecolind.2023.110860 Chollet-villalpando JG, Barrows FT, Mclean E (2025) Body shape variation in Atlantic Salmon ( Salmo salar , L.) fed fishmeal and fish oil-free diets. Fishes 10:8–13. https://doi.org/https://doi.org/10.3390/fishes10020062 Coure SB, Shelke AN, More MS (2025) A comprehensive review of morphometric and meristic variations in freshwater fishes: Trends, environmental drivers and taxonomic implications. Asian J Fish Aqua Res 27:36–48. https://doi.org/https://doi.org/10.9734/ajfar/2025/v27i121035 Cozzolino D, Power A, Chapman J (2019) Interpreting and reporting principal component analysis in food science analysis and beyond. Food Analy Meth 12:2469–2473. https://doi.org/10.1007/s12161-019-01605-5 Delfina AM, Saowalak Onming UNN (2017) Growth performance, genetic diversity and morphometric traits of an introduced wild and hatchery population of Clarias macrocephalus (Gunther, 1864). J Fish Env 41:1–19 Du G, Shi J, Zhang J, Ma Z, Liu X, Yuan C, Zhang B, Zhang Z, Harrison MD (2021) Exogenous probiotics improve fermentation quality, microflora phenotypes, and trophic modes of fermented vegetable waste for animal feed. Micro 9:1–18. https://doi.org/10.3390/microorganisms9030644 Egerton S, Culloty S, Whooley J, Stanton C, Ross RP (2018) The gut microbiota of marine fish. Front Microbio 9:1–17. https://doi.org/10.3389/fmicb.2018.00873 Fassatoui C, Shaiek M, Salah M (2024) Assessing shell morphology in freshwater mussels from the Maaden River Tunisia: Insights from geometric morphometrics and shape descriptors. Sci Afr 26:1–14. https://doi.org/10.1016/j.sciaf.2024.e02427 Hadijah H, Loar L, Mardiana M, Kantun W, Zainuddin Z (2024) Dietary probiotics and its effect on growth rate, survival rate, and feed conversion ratio of Clarias gariepinus . Jur Riset Akua 18:227–238. https://doi.org/10.15578/jra.18.4.2023.227-238 Hasan I, Gai F, Cirrincione S, Rimoldi S, Saroglia G, Terova G (2023) Chitinase and insect meal in aquaculture nutrition: A comprehensive overview of the latest achievements. Fishes 8:1–16. https://doi.org/10.3390/fishes8120607 Hasan R, Hossain MA, Islam MR, Iqbal MM (2021) Does commercial probiotics improve the growth performance and hematological parameters of Nile tilapia ( Oreochromis niloticus )? Aqua Res 4:160–168. https://doi.org/10.3153/ar21013 Hunter JD (2007) Matplotlib: A 2D Graphics Environment. Sci Prog 90–95 Hussein AR, Younes GO, El-dakdouki MH (2025) Impact of environmental conditions on allometric and morphometric traits of fish in Jiyeh, Lebanon : A multivariate analysis. Egy J Aqua Res 51:546–554. https://doi.org/10.1016/j.ejar.2025.08.002 Ines F, Schroeder R, Mugerza E, Oyarzabal I, Mccarthy ID (2024) Chelidonichthys lucerna (Linnaeus,1758) population structure in the northeast atlantic inferred from landmark-based morphometry. Bio 13:1–13. https://doi.org/https://doi.org/10.3390/biology13010017 Kenneth A, Janet A, Emily H, Thomás NS, Elizabeth A, Klaus B, Rose KA, Holsman K, Nye JA, Markowitz EH, Banha TNS, Bednaršek N, Bueno-pardo J, Deslauriers D (2024) Advancing bioenergetics-based modelling to improve climate change projections of marine ecosystems. Mar Eco Prog Ser 732:193–221. https://doi.org/10.3354/meps14535 Kulzer RG, Silva RM, Rocha AF, Seabra RC, Rocha E, Erzini K, Correia AT (2026) Population structure of the european seabass ( Dicentrarchus labrax ) in the Atlantic Iberian coastal waters inferred from body morphometrics and otolith shape analyses. Fish 11:1–19. https://doi.org/https://doi.org/10.3390/fishes1101001 Le Cren E (1951) The Length-Weight relationship and seasonal cycle in gonad weight and condition in the Perch. Brit Ecol Soc 20:201–219. https://doi.org/http://www.jstor.org/stable/1540 Lingam SS, Sawant PB, Chadha NK (2019) Duration of stunting impacts compensatory growth and carcass quality of farmed milkfish, Chanos chanos (Forsskal, 1775) under field conditions. Sci Rep 9:1–11. https://doi.org/10.1038/s41598-019-53092-7 Long WC (2026) Ecology ocean acidification reduces juvenile snow crab ( Chionoecetes opilio ) survival but does not affect growth or morphometrics. J Exp Mar Bio Ecol 594:1–7. https://doi.org/10.1016/j.jembe.2025.152153 Lorena Martinez-Leiva JML, Effrosyni F, Javier D, Santiago H, Javier RVMT (2023) Energetic implications of morphological changes between fish larval and juvenile stages using geometric morphometrics of body shape. Anim 13:1–13. https://doi.org/https://doi.org/10.3390/ani13030370 Majeed S, Gu M, Lani N, Kim H, Soo H, Jin M, Jawad LA, Yeon D, Myun J (2026) Regional studies in marine science distinguishing three flounder species in the East Sea of Korea using otolith and body morphometric analysis. Reg Stud Mar Sci 93:1–12. https://doi.org/10.1016/j.rsma.2025.104714 Mathias M, Veiga-malta T, Storr-paulsen M, Sousa L, Feekings J (2026) Using morphometric relationships for total length prediction of Atlantic cod ( Gadus morhua ) in electronic monitoring videos. Fish Res 293:1–10. https://doi.org/10.1016/j.fishres.2025.107633 Mehar M (2019) Fish trait preferences:A review of existing knowledge and implications for breeding programmes. Rev Aqua 12:1–24. https://doi.org/10.1111/raq.12382 Melenchón F, de Mercado E, Pula HJ, Cardenete G, Barroso FG, Fabrikov D, Lourenço HM, Pessoa MF, Lagos L, Weththasinghe P, Cortés M, Tomás-almenar C (2022) Fishmeal dietary replacement up to 50%: A comparative study of two insect meals for Rainbow trout ( Oncorhynchus mykiss ). Anim 12:1–22. https://doi.org/10.3390/ani12020179 Pavlov DA (2015) Condition and health indicators of exploited marine fishes. Mar Bio Res 11:110–112. https://doi.org/10.1080/17451000.2014.904886 Pedregosa F, Weiss R, Brucher M (2011) Scikit-learn: Machine learning in Python. J Mach Learn Res 12:2825–2830 Pereira WA, Mendonça CMN, Urquiza AV, Marteinsson VÞ, LeBlanc JG, Cotter PD, Villalobos EF, Romero J, Oliveira RPS (2022) Use of probiotic bacteria and bacteriocins as an alternative to antibiotics in aquaculture. Micro 10:1–23. https://doi.org/10.3390/microorganisms10091705 Randazzo B, Zarantoniello M, Cardinaletti G, Cerri R, Giorgini E, Belloni A, Contò M, Tibaldi E, Olivotto I (2021) Hermetia illucens and poultry byproduct meals as alternatives to plant protein sources in gilthead seabream ( Sparus aurata ) diet: A multidisciplinary study on fish gut status. Anim 11:1–22. https://doi.org/10.3390/ani11030677 Robins H, Chapuis L, Kerr CC, Dutka T, Donald J, Collin SP (2025) The inner ear of the Port Jackson shark, ( Heterodontus portusjacksoni ): Morphometric analysis using bioimaging and phalloidin staining. Hear Res 466:1–13. https://doi.org/10.1016/j.heares.2025.109368 Rossum GV (2025) The Python Language. The Python Foundation. https://www.python.org/ Saleh A, Hasan M, Raadsma HW, Khatkar MS, Jerry DR, Rahimi M (2024) Aquacultural engineering prawn morphometrics and weight estimation from images using deep learning for landmark localization. Aqua Eng 106:1–13. https://doi.org/10.1016/j.aquaeng.2024.102391 Silva SSDE, Davy FB (1992) Culture systems in Asia. Asian Fish Sci 5:129–144. https://doi.org/https://doi. org/10.33997/j.afs.1992.5.2.001 Strauss RE, Bookstein FL (1982) The truss: Body form reconstructions in morphormetrics. Syst Zoo 31:113–135. http://sysbio.oxfordjournals.org/ Sutthakiet O, Koonawootrittriron S, Sukhavachana S, Chatchaiphan S (2020) Heritability and genetic correlation of body shape and deformity in snakeskin gourami ( Trichopodus pectoralis Regan , 1910). Aqua 523:1–5. https://doi.org/10.1016/j.aquaculture.2020.735208 Tabassum T, Sofi Uddin Mahamud AGM, Acharjee TK, Hassan R, Akter Snigdha T, Islam T, Alam R, Khoiam MU, Akter F, Azad MR, Al Mahamud MA, Ahmed GU, Rahman T (2021) Probiotic supplementations improve growth, water quality, hematology, gut microbiota and intestinal morphology of Nile tilapia. Aqua Rep 21:1–13. https://doi.org/10.1016/j.aqrep.2021.100972 Talijan I (2026) Ecological informatics deep learning approach to landmarking and measurement error analysis for gilthead seabream ( Sparus aurata ) origin classification in geometric morphometrics. Ecol Info 93:1–16. https://doi.org/10.1016/j.ecoinf.2025.103560 Terje J, Sistiaga M, Herrmann B (2026) Size limits for the use of Ballan wrasse ( Labrus bergylta ) as cleaner fish in Salmon aquaculture cages. Aqua 615:1–12. https://doi.org/10.1016/j.aquaculture.2025.743613 Third GM, Parsons DM (2024) Population identification of Snapper ( Chrysophrys auratus ) using body geometric morphometrics to inform sustainable fisheries management. Fish Res 280:1–10. https://doi.org/10.1016/j.fishres.2024.107159 Toledo A, Frizzo L, Signorini M, Bossier P, Arenal A (2019) Impact of probiotics on growth performance and shrimp survival: A meta-analysis. Aqua 500:196–205. https://doi.org/10.1016/j.aquaculture.2018.10.018 Traverso F, Aicardi S, Bozzo M, Zinni M, Amaroli A, Galli L, Candiani S, Vanin S, Ferrando S (2024) New insights into geometric morphometry applied to fish scales for species identification. Anim 14:1–19. https://doi.org/https://doi.org/10.3390/ani14071090 Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Walt SJ, Van Der Brett M, Wilson J, Millman KJ (2020) SciPy 1.0: Fundamental algorithms for scientific computing in Python. Natr Methds 17:261–272. https://doi.org/10.1038/s41592-019-0686-2 Wang Y, Xie Y, Li Y, Peng F, Li J, Jiang W, Xie B, Fu P (2025) Multivariate and geometric morphometrics reveal morphological variation among Sinibotia fish. Bio 14:1–18. https://doi.org/https://doi.org/10.3390/biology14091177 Wasso DS, Ayagirwe RBB, Chasinga TB, Ntagereka EM, Mweze AK (2025) Morphometric characteristics, genetic profile, and histology of Nile tilapia in Mwenga territory, South Kivu. Egy J Aqua Res xxx 1–10. https://doi.org/10.1016/j.ejar.2025.12.001 Weththasinghe P, Lagos L, Cortés M, Hansen JØ, Øverland M (2021) Dietary inclusion of black soldier fly ( Hermetia Illucens ) larvae meal and paste improved gut health but had minor effects on skin mucus proteome and immune response in Atlantic salmon ( Salmo salar ). Front Immuno 12:1–16. https://doi.org/10.3389/fimmu.2021.599530 Xiao B, Wu H, Wei Y (2018) Simple baselines for human pose estimation and tracking. Proceed Eur Conf Comp Vis (ECCV). 1:472–487. https://doi.org/10.1007/978-3-030-01231-1 Zuur AF, Ieno EN, Smith GM, Landes (2007) Mixed Effects Models and Extensions in Ecology with R. Stat Bio Heal. 4–30. https://doi.org/10.1007/978-0-387-87458-6 Additional Declarations No competing interests reported. Supplementary Files image1.png Graphical Abstract Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9672785","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638182037,"identity":"c0c3e93d-459f-463e-aefe-b7f1783a4d70","order_by":0,"name":"Thaddeus Zaabwe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYFACHiBiA9LsDQzMxOlgg2nhOUCyFokEIrXIz+89+OFNmU3i/JlvDD8XVNgw8Ld3J+DVwtjGlyw551xa4obbOcbSM86kMUicObsBrxZmNh4Dad62w4kbpHPADAYDiVz8WtjYeIx/87b9BzrsDIhBhBYeNh4zoOEHEhtugBlEaJFgyzGznHMu2XjDmbQya54zaTwE/SLffMb4xpsyO9n57Yc33+apsJHjb+/FrwUJcBiAXUqschBgf0CK6lEwCkbBKBhBAACuSEGiMfxLqgAAAABJRU5ErkJggg==","orcid":"","institution":"Mzuzu University","correspondingAuthor":true,"prefix":"","firstName":"Thaddeus","middleName":"","lastName":"Zaabwe","suffix":""},{"id":638182038,"identity":"64456c6e-9f23-49eb-ad1d-b4ed5915a9a6","order_by":1,"name":"Elias Chirwa","email":"","orcid":"","institution":"Mzuzu University","correspondingAuthor":false,"prefix":"","firstName":"Elias","middleName":"","lastName":"Chirwa","suffix":""},{"id":638182039,"identity":"59293bec-4200-4f15-85c1-1eda129a3617","order_by":2,"name":"Kumbukani Mzengereza","email":"","orcid":"","institution":"Mzuzu University","correspondingAuthor":false,"prefix":"","firstName":"Kumbukani","middleName":"","lastName":"Mzengereza","suffix":""},{"id":638182040,"identity":"b127d5e6-4b29-44fd-b4e7-693342e0763f","order_by":3,"name":"Jeremiah Kang’ombe","email":"","orcid":"","institution":"Lilongwe University of Agriculture and Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Jeremiah","middleName":"","lastName":"Kang’ombe","suffix":""},{"id":638182041,"identity":"ffb15bbe-cf9c-4340-953f-224069b4fa36","order_by":4,"name":"Georges Lufungula Alunga","email":"","orcid":"","institution":"Centre for Research in Biodiversity, Ecology, Evolution and Conservation","correspondingAuthor":false,"prefix":"","firstName":"Georges","middleName":"Lufungula","lastName":"Alunga","suffix":""},{"id":638182042,"identity":"33018549-caa3-4ae5-9060-f45dfa78bc6a","order_by":5,"name":"Prudence Agnandji","email":"","orcid":"","institution":"Food for All (Aliments Pour Tous)","correspondingAuthor":false,"prefix":"","firstName":"Prudence","middleName":"","lastName":"Agnandji","suffix":""},{"id":638182043,"identity":"e1540b7b-a97f-4896-aebd-3e5a2b32a35d","order_by":6,"name":"Issa Balde","email":"","orcid":"","institution":"Mzuzu University","correspondingAuthor":false,"prefix":"","firstName":"Issa","middleName":"","lastName":"Balde","suffix":""},{"id":638182044,"identity":"fdb85ea3-c5e2-4c72-b0db-4bafbd8b0d52","order_by":7,"name":"Salumu Kimwanga","email":"","orcid":"","institution":"Mzuzu University","correspondingAuthor":false,"prefix":"","firstName":"Salumu","middleName":"","lastName":"Kimwanga","suffix":""},{"id":638182045,"identity":"356c5f8b-8946-44fe-b1e1-4b4007f91306","order_by":8,"name":"Ndong Ngagne","email":"","orcid":"","institution":"Cheikh Anta Diop University","correspondingAuthor":false,"prefix":"","firstName":"Ndong","middleName":"","lastName":"Ngagne","suffix":""},{"id":638182046,"identity":"ae8f1274-718b-49a3-84c9-68da16c3c261","order_by":9,"name":"Denis Opiyo","email":"","orcid":"","institution":"National Agricultural Research Organization, National Fisheries Resources Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Denis","middleName":"","lastName":"Opiyo","suffix":""}],"badges":[],"createdAt":"2026-05-10 21:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9672785/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9672785/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109289652,"identity":"62273027-0a83-4588-81f4-733cae021f07","added_by":"auto","created_at":"2026-05-15 06:34:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":624468,"visible":true,"origin":"","legend":"\u003cp\u003ePositions of the biological landmarks (red dots) of Redbreastedtilapia fingerlings taken from inoculation level T3. The landmarks were generated from TpsDig2 v2.31 software.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9672785/v1/09258c1cd9b4858689d83809.png"},{"id":109296905,"identity":"1f378767-5462-4ce7-b3ba-6ede7471d3ed","added_by":"auto","created_at":"2026-05-15 08:52:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21403,"visible":true,"origin":"","legend":"\u003cp\u003eBody shape variation across PC 1 covariance matrix scores\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9672785/v1/78d179c74215981a08cfea2a.png"},{"id":109289655,"identity":"8bd7c034-fdd7-40b5-b2cc-2399fd64f94e","added_by":"auto","created_at":"2026-05-15 06:34:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":19437,"visible":true,"origin":"","legend":"\u003cp\u003eA truss network of interconnected morphometric trait distances of Redbreasted tilapia\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9672785/v1/020d79e7018189b206ca426d.png"},{"id":109289656,"identity":"d1181434-4310-46d3-9330-8d5e8d54871d","added_by":"auto","created_at":"2026-05-15 06:34:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69801,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplot for growth parameter scores\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9672785/v1/2b51e72e30deb27b91344810.png"},{"id":109296242,"identity":"af9d3aae-fcc6-4b75-9533-b3cf1660fc42","added_by":"auto","created_at":"2026-05-15 08:46:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":562250,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots for growth parameters (mean ± SE). \u003cstrong\u003eA\u003c/strong\u003e is the AFCR, \u003cstrong\u003eB\u003c/strong\u003e is the SGR, and \u003cstrong\u003eC\u003c/strong\u003e is weight gain.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9672785/v1/8f031debfea1955408a842a1.png"},{"id":109289658,"identity":"e8e04934-9a8f-45d8-9981-8ec6dc68a446","added_by":"auto","created_at":"2026-05-15 06:34:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":102533,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelogram heatmap for Coptodon rendalli morphometric traits\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-9672785/v1/d42c232282d046d80a7a6ed6.png"},{"id":109289660,"identity":"8a25c81d-83fd-4f37-90fd-665be03bf306","added_by":"auto","created_at":"2026-05-15 06:34:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":47680,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplot of scores and morphometric loadings\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-9672785/v1/49af9b5ab4a6ccbe4474fea2.png"},{"id":109296382,"identity":"36e62e78-8922-432b-8f8f-374bedd80168","added_by":"auto","created_at":"2026-05-15 08:46:45","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":163679,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9672785/v1/d7d8652a6bc55e551ee17860.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Probiotic-enriched black soldier fly larvae meal diets impact the relationship between morphometric traits and the growth of Redbreasted tilapia (Coptodon rendalli)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMorphometric analysis, which is the quantitative analysis of body shape, length, and weight, forms the core of aquaculture and fisheries science, as it reveals the growth status, health, and ecological adaptation of fish populations (Kenneth et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This involves the use of ecological informatics by providing precise tools for shape analysis, especially in fish (Talijan \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Fish deformities and body shape are important qualities for fish selection in aquaculture production; however, they are usually secondary to other growth parameters (Sutthakiet et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, morphometric traits offer evidence of how well habitats support the functions of aquatic organisms (Hussein et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In aquaculture fish selection programs, a trend has evolved whereby these traits are incorporated to predict fish performance. Morphometric traits significantly affect the preferences of consumers and their willingness to pay for fish (Mehar \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The most commonly used morphometrics include the length‒weight relationship and condition indices to provide inferences on body condition, energy stores, physiological fitness, and quality of growth of cultured and wild fish stocks (Brosset et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Morphometric measures are important in that they combine variables of size, shape, and energy reserves, thereby providing estimations of nutritional status, health, and biomass productivity that are often more sensitive than simple measures of body weight alone (Traverso et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, morphometric traits are typically pseudo representations of fish condition, health, and biomass productivity, and they yield more information than just length or weight. They have positive impacts on growth performance, feed utilization, antioxidant status, immune functionality, and the gut microbiota in a number of cultured fish species, such as tilapia, seabass, and catfish (Wang et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The use of geometric morphometric methods enables solid visualization and measurement of morphological changes by separating the shape and non-shape parts and the ability to produce biologically significant morphological changes (Coure et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In some cases, fish morphology has been used to determine fish escape from culture systems such as cages (Terje et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Nonetheless, morphometric responses, including shifts in shape or condition indices compared with growth performance, are still poorly investigated. For example, while recent work on Atlantic salmon feeding trials explored shape variation under divergent protein sources, the short time frames, limited sample sizes, and limited definitive conclusions about diet-induced morphometric responses (Chollet-Villalpando et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough fish growth parameters such as the apparent feed conversion ratio (AFCR), protein efficiency ratio (PER), specific growth rate (SGR), weight gain, and relative condition factor (\u003cem\u003eKn\u003c/em\u003e) have been extensively used over time, a significant gap in the research concerning their combination with deep morphometric responses when probiotics and insect meals are used in the world of aquaculture nutrition. These measures quantify gross changes in mass and feed efficiency but do not measure indirect morphological changes in body shape or structure, which can help to identify other effects of dietary treatments on fish physiology (Pamphile et al. 2024). This is further overstated in research that involves the use of insect meals to replace fish meal as a substitute ingredient and in probiotic-inoculated diets (Torres-Maravilla et al. 2024). Additionally, the effects of insect-based diets on growth performance and health parameters in different fish species have been demonstrated. However, limited emphasis has been given to morphological changes in fish. Insects such as black soldier fly (BSF) larvae, when used in aquaculture, improve their histology, reduce enterocyte steatosis in the pyloric caeca, and reduce IgM levels in the distal intestine of Atlantic salmon (\u003cem\u003eSalmo salar\u003c/em\u003e) (Weththasinghe et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, BSFL diets improve lipid deposition and fish welfare in gilt heads (Randazzo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and rainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e) (Melench\u0026oacute;n et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Probiotics contribute to good fish performance, but this has been limited to putative (naturally isolated) probiotics. Fish rely on the microbiota to digest non-starch polysaccharides such as chitin (Egerton et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and their digestion is greatly enhanced when probiotics are utilized (Hasan et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our present study examined the effects of exogenous probiotic inoculation of black soldier fly diets on morphometric responses, specific growth parameters, and their relationships with Redbreasted tilapia reared in concrete fish tanks. The findings will generate valuable scientific evidence, which will guide the imminent rearing of Redbreasted tilapia (\u003cem\u003eCoptodon rendalli\u003c/em\u003e) and other cichlid species.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area, fish husbandry, and sampling\u003c/h2\u003e \u003cp\u003eThe study was carried out at the Domasi Aquaculture Centre in Zomba District, southern Malawi, which is located at coordinates of 11\u0026deg; 46' 703\u0026rdquo; S, 34\u0026deg; 13' 082\u0026rdquo; E.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFish husbandry\u003c/h3\u003e\n\u003cp\u003eIn total, 480 Redbreasted tilapia fingerlings weighing 4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 grams were sourced from the Domasi Aquaculture Centre hatchery, Zomba district, Southern Malawi, and and used in the experiment. The fish were harvested and conditioned for 1 week in 2 m\u0026sup3; concrete fish tanks and fed a 40% crude protein exogenous diet.\u003c/p\u003e\n\u003ch3\u003eProbiotic feed preparation\u003c/h3\u003e\n\u003cp\u003eExogenous Sanolife\u0026reg; PRO-2 probiotic mixtures (\u003cem\u003eBacillus subtilis, Bacillus licheniformis, and Bacillus pumilis\u003c/em\u003e obtained from INVE Aquaculture, Belgium) were used at T0 [0 CFU/kg] as a control, T1 [4\u0026times;10\u003csup\u003e10\u003c/sup\u003e CFU/kg] as treatment 1, T2 [8\u0026times;10\u003csup\u003e10\u003c/sup\u003e CFU/kg] as treatment 2, and T3 [12\u0026times;10\u003csup\u003e10\u003c/sup\u003e CFU/kg] as treatment 3. The probiotic concentrations were homogenized in 2 litres of fish oil. The formulated feed pellets were top-dressed evenly with the prepared mixture. The pellets were left to absorb the probiotic mixture for a period of 30 minutes to 2 hours at room temperature (25\u0026deg;C). Top dressing was applied at intervals of 3 days for the entire culture period, as this was the ideal maximum time at which the probiotics would remain attached to the pellets.\u003c/p\u003e\n\u003ch3\u003eDiet formulation\u003c/h3\u003e\n\u003cp\u003eA 42% crude protein isonitrogenous and isocaloric BSFL-based probiotic-inoculated fish diet was formulated using locally available ingredients sourced from Mzuzu market, Mzuzu City, northern Malawi. BSFL was sourced from the BSF culture unit at the Department of Fisheries and Aquatic Sciences at Mzuzu University, Malawi. Prior to feed formulation, the BSFL and sourced ingredients were subjected to proximate analysis for crude protein, crude lipid, crude fibre, nitrogen-free extract, and ash contents while adopting the standard methods of AOAC (2000). The crude protein content was determined via the Kjeldahl system (Kjeltec\u0026trade; 8400-Autoanalyzer, FOSS, Denmark), the crude lipid content was determined by the Soxhlet method, and the crude ash content was determined through combustion in a muffle oven, which involves oven incineration at 550\u0026deg;C for 5 hours. The moisture content was also determined through oven drying at 105\u0026deg;C. The pulverized ingredients were homogenized using a clean spade and extruded at approximately 90\u0026ndash;120\u0026deg;C\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\)\u003c/span\u003e\u003c/span\u003e using an electric-powered pelletizer (Model: Y2-160 M-4, Dongxiang Hongxiang, China) to form 1 mm feed pellets. The formulated pellets were spread on clean synthetic tarpaulin and dried under shade for four days to reduce moisture. The inclusion levels of the formulated diet are indicated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The formulated BSFL diet was subjected to proximate analysis before its application in the experiment. This was done to control for any quality checks in the feed, according to the dietary nutrient requirements of the fish. The feed was processed and stored in a well-aerated room at 25\u0026deg;C.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIngredient inclusion levels for the 42% CP probiotic inoculated BSFL fish feed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIngredient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInclusion level (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefatted black soldier fly larval meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeoiled soybean meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheat flour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheat bran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin premix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocalcium phosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStabilized Vitamin C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChitinase enzyme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eProximate composition (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude lipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude fibre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigestible Energy (Kcal/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4082.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ePremix analysis per 2000 g; vitamin An 11,500 IU, vitamin D\u003c/em\u003e \u003csub\u003e \u003cem\u003e3\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e3,000 IU, vitamin E 10,000 IU, vitamin K 2,000 mg, vitamin B\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e1,000 mg, vitamin B\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e4,000 mg, vitamin B6 500 mg, vitamin B12 6000 mcg, choline 150000 mg, iron 30000 mg, manganese 80000 mg, copper 5000 mg, zinc 50000 mg, cobalt 225 mg, iodine 1000 mg, selenium 150 mg, lysine 20000 mg, methionine 25000 mg, phosphorus 5250 mg and calcium 750 mg, calcium: 2.057%, phosphorus: 1.08%.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eExperimental setup\u003c/p\u003e \u003cp\u003eEach concrete tank measuring 2\u0026times;1\u0026times;1 m was stocked with 40 fingerlings during the onset of the experiment. Probiotic inoculation levels were determined in triplicate (\u003cem\u003en\u0026thinsp;=\u0026thinsp;3\u003c/em\u003e). The fish were fed \u003cem\u003ead libitum\u003c/em\u003e throughout the experiment. When feed utilization was not efficient, adjustments were made in relation to the proportionate average body weights. The experiment was carried out in a completely randomized design for 72 days. To maintain a clean environment, the fish tanks were routinely cleaned biweekly to eliminate any fecal matter and uneaten feed.\u003c/p\u003e \u003cp\u003eFish sample collection\u003c/p\u003e\n\u003ch3\u003eBody condition indices\u003c/h3\u003e\n\u003cp\u003eThe relative condition factor (\u003cem\u003eKn\u003c/em\u003e) was analysed using Le Cren's (1951) formula:\u003c/p\u003e \u003cp\u003eKn\u0026thinsp;=\u0026thinsp;W\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:∕\\)\u003c/span\u003e\u003c/span\u003eaL\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe specific growth rate (%\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:∕\\)\u003c/span\u003e\u003c/span\u003eday) was calculated via the following formula:\u003c/p\u003e \u003cp\u003eSGR (%\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:∕\\)\u003c/span\u003e\u003c/span\u003eday)\u0026thinsp;=\u0026thinsp;Ln final weight \u0026ndash; Ln initial weight\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:∕\\)\u003c/span\u003e\u003c/span\u003etime (days) \u0026times; 100\u003c/p\u003e \u003cp\u003eWeight gain was analysed via the formula described by Silva \u0026amp; Davy (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWeight gain (g) = Wt\u003csub\u003e2\u003c/sub\u003e \u0026ndash; Wt\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImage acquisition and shape analysis\u003c/h2\u003e \u003cp\u003eAt the end of the experiment, a total of 30 fish fingerlings per inoculation level were harvested from the fish tanks and placed in 10-liter water-filled clean buckets. To minimize fish stress, the fish were immobilized in 40 mg/L dissolved clove solution following the protocols used by Anderson \u0026amp; McKinley (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). This was conducted until stage 4 of anaesthesia was achieved (rare opercular movements, relaxed muscle tone, and no reaction to stimuli).\u003c/p\u003e \u003cp\u003eThe Truss Network system standard protocol was used to analyse body morphometrics (Strauss \u0026amp; Bookstein \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). The method is non-destructive and well refined for any morphological variation across fish of various subpopulations (Ines et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It further helps in the estimation of, and compensation for, measurement error. The homologous landmarks are defined along the contours of the fish body while the distances between them are quantified. Each fish was laid on its left side against a white background, the fins were stretched out, and the fish were photographed from a fixed position of 40 mm at an elevated angle of 90\u0026deg;. The images were captured using a Canon DS126761 high-resolution digital camera (Canon Zoom Lens EF-S 18\u0026ndash;55 mm, φ58 mm; Canon Inc. 3\u0026ndash;30\u0026ndash;2, Shinomaruko, Ohta-ku, Tokyo, Japan). In total, 20 anatomical landmarks were identified, which defined 20 distances as reflected in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The captured images per inoculation level were imported into TpsUtil software (v1.78) to generate an image file. The generated image files were imported into TpsDig software (v2.31) for landmarking (digitization). These software programs help in the actual placement and recording of X and Y landmarks, thus enabling the construction of a truss network.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe morphometric traits, landmarks, their numbers, and locations are described in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The upper part of the table describes the landmark, the morphometric trait, and its abbreviation, whereas the lower part describes the number and location.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMorphometric distances and landmarks described along the body contour of Redbreasted tilapia used for body shape analysis. Please refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e as well.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eBody landmarks\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandmark\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMorphometric trait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 to 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHead Length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 to 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 to 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDorsal Fin Length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDSFL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 to 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaudal Fin Length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8 to 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnal Fin Length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAFL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10 to 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePelvic Fin Length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePVFL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 to 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOperculum Length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14 to 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance Between Eyes (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDBE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16 to 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEye Diameter (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eED\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18 to 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePectoral Fin Length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCFL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 to 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody Depth (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLocation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnterior tip of the fish snout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnterior point of the dorsal fin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosterior tip of the dorsal fin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerminal part of the dorsal fin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnterior part of the caudal fin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosterior tip of the caudal fin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosterior end of anal fin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosterior tip of the anal fin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnterior part of the anal fin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosterior tip of the pelvic fin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnterior part of the pelvic fin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower part of the operculum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper part of the operculum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnterior part of the distance between eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosterior part of the distance between eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnterior point of eye diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosterior point of eye diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnterior part of the pectoral fin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosterior part of the pectoral fin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoint of body depth measurement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe body shape variation across PC1 covariance matrix scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was used to generate a wireframe (shape variation) diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) with demarcated distances. This was carried out using the generalized Procrustes analysis (GPA) for all landmarks under each section, which were pooled together via MorphoJ v1.70a software. To minimize the binary cross-entropy between the predicted heatmap \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sup\u003e and the ground truth heatmap \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\widehat{h}\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sup\u003e for each landmark \u003cem\u003ek\u003c/em\u003e, the loss function was formalized via the following equation:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:L=\\frac{1}{KHW}\\sum\\:_{k}\\sum\\:_{xy}-{h}_{xy}^{x}.log{\\widehat{h}}_{xy}^{k}-\\left(1-{h}_{xy}^{k}\\right).log\\left(1-{\\widehat{h}}_{xy}^{k}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ek\u003c/em\u003e denotes one of the K landmarks and where \u003cem\u003ex\u003c/em\u003e and y represent the height (H) \u0026times; width (W) of the sized image.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eMultivariate morphometric analysis and growth parameters\u003c/h2\u003e \u003cp\u003eA multivariate statistical approach of principal component analysis (PCA) was used to determine the physiological state of Redbreasted tilapia across inoculation levels and estimate the extensive relationships between all the morphometric traits and growth parameters. To maximize variance in loadings, orthogonal rotation was performed via varimax rotation (Cozzolino et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Multiple variables were summarized using the shared correlation structure to derive principal components. Each variable was scaled and centered. To study the statistical effect of individual explanatory variables on the morphometric body condition index, initial tests for multicollinearity between morphometric traits relying on the variance inflation factor (VIF) with values\u0026thinsp;\u0026lt;\u0026thinsp;5 were carried out (Zuur et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). A generalized linear modelling (GLM) approach with a Gaussian distribution and a log link function (Xiao et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was used to estimate the distributions of growth parameters and morphometric traits across inoculation levels. The extracted growth parameters and morphometric traits were subjected to normality and homogeneity of variance checks using Shapiro‒Wilk\u0026rsquo;s test and Levene\u0026rsquo;s test, respectively. One-way analysis of variance (ANOVA) was conducted to test for statistically significant differences between inoculation levels for morphometric traits and selected growth variables. Tukey\u0026rsquo;s \u003cem\u003ehonestly significant difference (HSD) post hoc test\u003c/em\u003e was used to separate statistically significant differences across probiotic inoculation levels. Single-variable linear regression was conducted to determine the functional relationships between the extracted morphometric traits and growth parameters. Statistical significance was considered at α\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Statistical analyses were conducted in Python programming v3.12.2 (Rossum \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) via the SciPy (Virtanen et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Matplotlib (Hunter \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), Scikit-learn (Pedregosa et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and PtitPrince libraries.\u003c/p\u003e \u003cp\u003eThe linear regression and principal component analysis equation models are described in equations \u003cb\u003ei\u003c/b\u003e and \u003cb\u003eii\u003c/b\u003e, respectively.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\widehat{\\gamma\\:}={\\beta\\:}_{0}+{\\beta\\:}_{i}X$$\u003c/div\u003e\u003c/div\u003e(i) \u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\widehat{\\gamma\\:}\\)\u003c/span\u003e\u003c/span\u003e represents the observed values of the growth parameter, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e represents the intercept, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the coefficient, and \u003cem\u003eX\u003c/em\u003e represents all the observed values of the morphometric traits.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{PC}_{k}={a}_{k1}{X}_{1}+{a}_{k2}{X}_{2}+{a}_{k3}{X}_{3}+\\cdots\\:\\cdots\\:\\cdots\\:+{a}_{kn}{X}_{n}$$\u003c/div\u003e\u003c/div\u003e(ii) \u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{PC}_{k}\\)\u003c/span\u003e\u003c/span\u003e represents the \u003cem\u003ek\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e principal component, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{1},\\:{X}_{2},\\:{X}_{3}\\dots\\:\\dots\\:\\dots\\:.{X}_{n}\\)\u003c/span\u003e\u003c/span\u003e represents the morphometric traits or growth variables, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{k1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{k2},\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{k3}\\)\u003c/span\u003e\u003c/span\u003e......... \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{kn}\\)\u003c/span\u003e\u003c/span\u003e represents the coefficients or loadings of morphometric traits and growth variables, and \u003cem\u003en\u003c/em\u003e represents the number of morphometric traits or growth variables.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGrowth parameters\u003c/h2\u003e \u003cp\u003eThe PCA biplot scores revealed that morphometric traits and growth-related parameters were interstructured, with PC1 as the major factor, with an explanation of 51.54% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This finding indicated that total growth performance was the most prevalent influence on phenotypic differentiation across treatments. PC2, which was associated with the AFCR and SGR, accounted for 23.90% of the total variance. The observed morphological divergence indicated an inclination toward greater inoculation, which was correlated with increased growth, indicating treatment-dependent morphological divergence. Weight gain, SGR, and the AFCR were retained after rotation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe ANOVA results for the retained growth parameters are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The T2 inoculation level had the highest mean values for SGR (3.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07) and weight gain (9.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21), with T0 having the lowest mean values of 1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 and 4.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14, respectively (B \u0026amp; C). This was indicative of increased fish growth when T2-inoculated feed was used to feed the fish. In contrast, T2 had the lowest mean AFCR (1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03), with T0 having the highest AFCR (2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05) (A). The lowest AFCR was associated with better feed utilization by the fish. Statistically significant differences across inoculation levels were shown for SGR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), weight gain (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and AFCR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMorphometric traits\u003c/h2\u003e \u003cp\u003eThe correlogram heatmap indicates that all morphometric variables were highly correlated, with the lowest correlation reflected between the operculum and distance between eyes, with a Pearson\u0026rsquo;s correlation coefficient (R) of 0.80 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The majority of morphometric traits were strongly correlated with each other, indicating covariance with size in general. Intercorrelations between total length, operculum length, pelvic fin length, pectoral fin length, and head length presented the highest Pearson\u0026rsquo;s correlation coefficient, R\u0026thinsp;=\u0026thinsp;0.99. The results from PCA indicated that the data were suitable for PCA reduction, with a Kaiser‒Meyer‒Olkin test value of 0.87 and a Bartlett\u0026rsquo;s test of sphericity, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, indicating that the variables were related.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe PCA biplot revealed a distinct separation of the effects of inoculation on morphometric traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). PC1 and PC2 accounted for 93.87% and 3.47% of the total variation, respectively. PC1 was highly related to total length, weight gain, body depth, and fin dimensions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese findings indicated that overall growth-related morphometric traits were affected mainly by the inoculation of probiotics. PC2 represents the secondary shape variation. The variables that were extracted after PCA orthogonal rotation included operculum length, eye diameter, pectoral length, total length, and distance between eyes.\u003c/p\u003e \u003cp\u003eThe results for the extracted traits are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The highest mean values for TL, ED, DBE, OL, and PFL were recorded at the T2 inoculation level. Statistically significant differences in morphometric traits were observed across all inoculation levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, statistically significant polynomial contrasts were indicated across inoculation levels for all morphometric traits (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SE) values of morphometric traits across inoculation levels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eInoculation level (cfu/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003ePolynomial contrasts\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorphometric trait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQuadratic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCubic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTL (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eED (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBE (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOL (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFL (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Values in rows with different superscripts are statistically significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 according to Tukey\u0026rsquo;s HSD \u003cem\u003epost hoc test\u003c/em\u003e. \u003cem\u003eNOTE: TL \u0026ndash; Total Length, ED \u0026ndash; Eye Diameter, DBE \u0026ndash; Distance between Eyes, OL \u0026ndash; Operculum Length, and PFL \u0026ndash; Pectoral Fin Length\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHierarchical clustering of the inoculation levels (T0, T1, T2, and T3) on the basis of the mean morphometric parameters (Euclidean distance and Ward linkage method) is depicted by the dendrogram in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Two major clusters are reflected: Cluster 1 (T0 and T1) and Cluster 2 (T2 and T3). The clusters converge at a far greater Euclidean distance (~\u0026thinsp;45), suggesting great overall morphometric dissimilarity between the low inoculation levels (T0 and T1) and the high inoculation levels (T2 and T3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eT0 and T1 are relatively small distances from each other (~\u0026thinsp;14), implying that the fish in T0 and T1 have morphometric profiles that can be considered relatively similar. T2 and T3 are concentrated at the intermediate level (~\u0026thinsp;22), hence being similar to more pronounced morphometric reactions. The results indicate that the morphological response to inoculation with T2 was dose dependent, and the greatest increase in morphometric parameters was found for all the parameters. The dendrogram supports multivariate separation as well as robust inoculation-induced structural partitioning of morphometric traits at the level of inoculation.\u003c/p\u003e \u003cp\u003eThe regression results between the PCA-extracted growth parameters and morphometric traits are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Positive Pearson\u0026rsquo;s correlation (R) values were observed between growth parameters and morphometric traits. Similarly, positive coefficients (\u003cem\u003eb\u003c/em\u003e) were observed between the growth parameters of weight gain and SGR across all the morphometric traits. Morphometric traits positively influenced weight gain and SGR. However, the AFCR negatively influenced morphometric traits, as the results indicated negative coefficients (\u003cem\u003eb\u003c/em\u003e). Overall, the regression model was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression of extracted growth parameters and extracted morphometric traits\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorphometric trait vs Growth parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eAdjusted\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTL (cm) vs WG (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eED (cm) vs WG(g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBE (cm) vs WG(g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOL (cm) vs WG (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFL (cm) vs WG (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTL (cm) vs SGR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eED (cm) vs SGR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBE (cm) vs SGR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOL (cm) vs SGR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFL (cm) vs SGR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFCR vs TL (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e86.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-7.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFCR vs ED (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFCR vs DBE (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFCR vs OL (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFCR vs PFL (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study focused on testing the hypothesis that morphometric traits and growth parameters such as the SGR, AFCR, \u003cem\u003eKn\u003c/em\u003e, and weight gain of Redbreasted tilapia are improved when the fish are fed probiotic-inoculated black soldier fly meal diets. Most studies have focused on the utilization of energy-dense variables such as fatty acids, proteins, and hormones to determine morphometric trait performance in fish. Morphometric traits act as representations of the energy-dense performance of a fish. The results from our study revealed that the fish growth parameters were closely linked to the morphometric traits, although the AFCR negatively contributed to the performance of the morphometric traits. The high SGR, weight gain, and AFCR could be linked to the enhanced digestive efficiency as a result of probiotics producing extracellular enzymes such as proteases, cellulases, amylases, and lipases. The enzymes efficiently breakdown proteins such as chitin, carbohydrates, and lipids, leading to increased nutrient absorption and hence facilitating faster growth (Hasan et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, during the stunting periods of Milkfish for compensatory cell growth, the high SGR and morphometric growth were similar to those reported in the present study (Lingam et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This finding supports the assumption that nutrient deprivation in fish affects their morphological traits (Pavlov \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Moreover, the morphological traits and growth parameters of SGR and weight gain decreased with limited probiotic inoculation. Similarly, the use of probiotics in insect aquafeeds increased shrimp fish weight and survival, as reflected in this study (\u0026gt;\u0026thinsp;95%) (Toledo et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Probiotics improve fish weight gain and feed utilization indices in addition to intestinal morphological modifications in fish, especially Nile tilapia (Tabassum et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), increasing protein intake and hence increasing SGR and weight gain, corroborating the findings of this study (Du et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similar to this work, the application of \u003cem\u003eTinebrio molitor\u003c/em\u003e in Nile tilapia fish diets, including \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e, did not significantly alter the SGR (Anany et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The high SGR and overall weight and performance values may be related to the synthesis of growth-promoting substances such as bacteriocins and other vitamins, particularly vitamin B complex, which improve the fish gut microbiota and function as natural growth promoters. The use of bacteriocins (Pereira et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and multivitamins (Asadollahi et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) as alternatives to antibiotics in aquaculture increases fish growth and overall fish morphometric traits, as seen in our study. In homogenous fish rearing environments, morphological variation decreases with somatic growth (Lorena et al. 2023). In contrast, our study revealed increased variability in the morphometry of fish with somatic growth. This could be attributed to the variation in probiotic inoculation levels, which improved nutrient availability, leading to increased growth of various morphometric traits. The addition of the appropriate amount of probiotics should improve fish growth and morphometric trait performance. The addition of undesirable probiotic inoculation levels in basal diets for fish growth can lead to low growth rates and yields coupled with low survival rates, as reflected in the rearing of \u003cem\u003eClarias gariepinus\u003c/em\u003e (Hadijah et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In our study, although T3 had a greater quantity than did T2, it was outperformed by the inoculation level T2. These findings suggest that the dosage for effective performance in Redbreasted tilapia in BSFL fish diets is ideal at the T2 level. The higher growth rate and morphometric traits in the probiotic-fertigated diets could be linked to the high digestive enzyme activities resulting from secretion by the diverse microbiota in the fish gut. The source of a fish can affect overall morphological trait performance. Fish that are usually sourced from the wild for culture do not show variability in morphometric growth (Delfina \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The fish utilized in this study were on-farm hatchery-raised fish with indications of morphological trait changes. However, morphometric trait analysis integrates external and internal phenotypic signals that increase the reliability of the findings on fish performance (Kulzer et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2026\u003c/span\u003e), auditory mechanisms (Robins et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and shell morphology in freshwater mussels (Fassatoui et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, the use of various morphometric traits effectively predicts variables such as total length, as used in our study, and regression models increase the number of morphometric traits that predict fish total length (Mathias et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) and weight (Saleh et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wasso et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Several authors acknowledge that morphometric traits such as body depth and eye size, as reflected in our study, indicate significant changes in fish morphological changes and can also predict fish growth parameters such as weight gain (Third \u0026amp; Parsons \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although geometric morphology can help in assessing homogeneity in morphological features (Binashikhbubkr et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), heterogeneity in fish morphometric traits across probiotic inoculation levels is notable. While our study revealed significant changes in morphometric traits and growth, some studies have shown contrasting results (no significant differences) in morphometric trait changes and fish growth, as indicated by Long (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). These differences could be attributed to the addition of probiotic inoculation to the feed used. Various morphometric traits are affected differently when fish are subjected to different rearing conditions and diets. For example, in our study, the extracted traits of operculum length, distance between eyes, eye diameter, and total length contrasted with body depth and snout length in a study conducted by Majeed et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of the present study provide evidence that the morphometric traits and selected growth parameters of Redbreatsed tilapia are influenced by the addition of probiotics to black soldier fly meal diets. This study highlights the novelty of the use of selected probiotics and black soldier flies and how they improve fish morphometric traits, growth, and their relationships. The study further revealed that morphometric and selected growth parameters improved with increased probiotic addition. This effect was most pronounced in the T2 probiotic inoculation treatment. This study revealed that morphometric traits and selected growth parameters were integrative and influenced when probiotics were added to black soldier fly diets. The use of morphometric variation to determine fish fitness and performance is recommended when commercial probiotics at the right inoculation levels are used.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the World Bank ACE II Additional Financing Project ID P176744 under the African Centre of Excellence for Neglected and Underutilized Biodiversity (ACE NUB) at Mzuzu University, Malawi.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by the World Bank ACE II Additional Financing Project ID P176744 under the African Centre of Excellence for Neglected and Underutilized Biodiversity (ACE NUB) at Mzuzu University, Malawi.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.Z. Conceptualized and conducted the research, performed data analysis and visualization, provided the software, and wrote the main manuscript text. E.C., K.M. and J.K. supervised, wrote and reviewed the manuscript. G.L.A., P.A., I.B., P.S.K and N.N. wrote the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThe authors are indebted to INVE Aquaculture, Belgium, for providing the commercial probiotic strain mixture used in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data are available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnany EM, Ibrahim MA, Abd IM, Razek E, Said E, Nabawy M, El, Amer AA, Zaineldin AI, Gewaily MS, Dawood MAO (2025) Combined effects of yellow mealworm (\u003cem\u003eTenebrio molitor\u003c/em\u003e) and \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e on the growth performance, feed utilization, intestinal health, and blood biomarkers of Nile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e) fed fish meal \u0026ndash; free diets. Prob Antimicro Prot 17:1387\u0026ndash;1398. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12602-023-10199-8\u003c/span\u003e\u003cspan address=\"10.1007/s12602-023-10199-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson WG, Mckinley RS (1997) The use of clove oil as an anaesthetic for rainbow trout and its effects on swimming performance. N Amer J Fish Mgt 17:1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/http://dx.doi.org/10.1577/1548-8675\u003c/span\u003e\u003cspan address=\"10.1577/1548-8675\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAOAC International (2000) Official methods of analysis of the AOAC International (17th ed)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsadollahi M, Baserh J, Abnaroodhelleh F, Kordyani MB, Samani MN, Dadar M (2025) Combined prebiotic and multivitamin supplementation enhances growth, survival, and disease resistance of Asian seabass in floating cages. Aqua Rep 43:1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.aqrep.2025.102919\u003c/span\u003e\u003cspan address=\"10.1016/j.aqrep.2025.102919\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBinashikhbubkr K, Babangida J, Al-misned F, Naim D (2024) Stock structure delineation of \u003cem\u003eKawakawa Euthynnus affinis\u003c/em\u003e (Cantor, 1849) from Malaysian Borneo using multivariate morphometric analysis. J King Saud Uni - Sci 36:1\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jksus.2024.103278\u003c/span\u003e\u003cspan address=\"10.1016/j.jksus.2024.103278\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrosset P, Averty A, Mathieu-resuge M, Schull Q, Soudant P, Lebigre C (2023) Fish morphometric body condition indices reflect energy reserves but other physiological processes matter. Ecol Ind 154:1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2023.110860\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2023.110860\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChollet-villalpando JG, Barrows FT, Mclean E (2025) Body shape variation in Atlantic Salmon (\u003cem\u003eSalmo salar\u003c/em\u003e, L.) fed fishmeal and fish oil-free diets. Fishes 10:8\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.3390/fishes10020062\u003c/span\u003e\u003cspan address=\"10.3390/fishes10020062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoure SB, Shelke AN, More MS (2025) A comprehensive review of morphometric and meristic variations in freshwater fishes: Trends, environmental drivers and taxonomic implications. Asian J Fish Aqua Res 27:36\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.9734/ajfar/2025/v27i121035\u003c/span\u003e\u003cspan address=\"10.9734/ajfar/2025/v27i121035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCozzolino D, Power A, Chapman J (2019) Interpreting and reporting principal component analysis in food science analysis and beyond. Food Analy Meth 12:2469\u0026ndash;2473. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12161-019-01605-5\u003c/span\u003e\u003cspan address=\"10.1007/s12161-019-01605-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelfina AM, Saowalak Onming UNN (2017) Growth performance, genetic diversity and morphometric traits of an introduced wild and hatchery population of \u003cem\u003eClarias macrocephalus\u003c/em\u003e (Gunther, 1864). J Fish Env 41:1\u0026ndash;19\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu G, Shi J, Zhang J, Ma Z, Liu X, Yuan C, Zhang B, Zhang Z, Harrison MD (2021) Exogenous probiotics improve fermentation quality, microflora phenotypes, and trophic modes of fermented vegetable waste for animal feed. Micro 9:1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/microorganisms9030644\u003c/span\u003e\u003cspan address=\"10.3390/microorganisms9030644\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgerton S, Culloty S, Whooley J, Stanton C, Ross RP (2018) The gut microbiota of marine fish. Front Microbio 9:1\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2018.00873\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2018.00873\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFassatoui C, Shaiek M, Salah M (2024) Assessing shell morphology in freshwater mussels from the Maaden River Tunisia: Insights from geometric morphometrics and shape descriptors. Sci Afr 26:1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.sciaf.2024.e02427\u003c/span\u003e\u003cspan address=\"10.1016/j.sciaf.2024.e02427\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHadijah H, Loar L, Mardiana M, Kantun W, Zainuddin Z (2024) Dietary probiotics and its effect on growth rate, survival rate, and feed conversion ratio of \u003cem\u003eClarias gariepinus\u003c/em\u003e. Jur Riset Akua 18:227\u0026ndash;238. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15578/jra.18.4.2023.227-238\u003c/span\u003e\u003cspan address=\"10.15578/jra.18.4.2023.227-238\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasan I, Gai F, Cirrincione S, Rimoldi S, Saroglia G, Terova G (2023) Chitinase and insect meal in aquaculture nutrition: A comprehensive overview of the latest achievements. Fishes 8:1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/fishes8120607\u003c/span\u003e\u003cspan address=\"10.3390/fishes8120607\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasan R, Hossain MA, Islam MR, Iqbal MM (2021) Does commercial probiotics improve the growth performance and hematological parameters of Nile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e)? Aqua Res 4:160\u0026ndash;168. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3153/ar21013\u003c/span\u003e\u003cspan address=\"10.3153/ar21013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunter JD (2007) Matplotlib: A 2D Graphics Environment. Sci Prog 90\u0026ndash;95\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHussein AR, Younes GO, El-dakdouki MH (2025) Impact of environmental conditions on allometric and morphometric traits of fish in Jiyeh, Lebanon : A multivariate analysis. Egy J Aqua Res 51:546\u0026ndash;554. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejar.2025.08.002\u003c/span\u003e\u003cspan address=\"10.1016/j.ejar.2025.08.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInes F, Schroeder R, Mugerza E, Oyarzabal I, Mccarthy ID (2024) \u003cem\u003eChelidonichthys lucerna\u003c/em\u003e (Linnaeus,1758) population structure in the northeast atlantic inferred from landmark-based morphometry. Bio 13:1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.3390/biology13010017\u003c/span\u003e\u003cspan address=\"10.3390/biology13010017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKenneth A, Janet A, Emily H, Thom\u0026aacute;s NS, Elizabeth A, Klaus B, Rose KA, Holsman K, Nye JA, Markowitz EH, Banha TNS, Bednaršek N, Bueno-pardo J, Deslauriers D (2024) Advancing bioenergetics-based modelling to improve climate change projections of marine ecosystems. Mar Eco Prog Ser 732:193\u0026ndash;221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3354/meps14535\u003c/span\u003e\u003cspan address=\"10.3354/meps14535\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKulzer RG, Silva RM, Rocha AF, Seabra RC, Rocha E, Erzini K, Correia AT (2026) Population structure of the european seabass (\u003cem\u003eDicentrarchus labrax\u003c/em\u003e) in the Atlantic Iberian coastal waters inferred from body morphometrics and otolith shape analyses. Fish 11:1\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.3390/fishes1101001\u003c/span\u003e\u003cspan address=\"10.3390/fishes1101001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe Cren E (1951) The Length-Weight relationship and seasonal cycle in gonad weight and condition in the Perch. Brit Ecol Soc 20:201\u0026ndash;219. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/http://www.jstor.org/stable/1540\u003c/span\u003e\u003cspan address=\"https://doi.org/http://www.jstor.org/stable/1540\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLingam SS, Sawant PB, Chadha NK (2019) Duration of stunting impacts compensatory growth and carcass quality of farmed milkfish, \u003cem\u003eChanos chanos\u003c/em\u003e (Forsskal, 1775) under field conditions. Sci Rep 9:1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-019-53092-7\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-53092-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLong WC (2026) Ecology ocean acidification reduces juvenile snow crab (\u003cem\u003eChionoecetes opilio\u003c/em\u003e) survival but does not affect growth or morphometrics. J Exp Mar Bio Ecol 594:1\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jembe.2025.152153\u003c/span\u003e\u003cspan address=\"10.1016/j.jembe.2025.152153\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLorena Martinez-Leiva JML, Effrosyni F, Javier D, Santiago H, Javier RVMT (2023) Energetic implications of morphological changes between fish larval and juvenile stages using geometric morphometrics of body shape. Anim 13:1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.3390/ani13030370\u003c/span\u003e\u003cspan address=\"10.3390/ani13030370\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMajeed S, Gu M, Lani N, Kim H, Soo H, Jin M, Jawad LA, Yeon D, Myun J (2026) Regional studies in marine science distinguishing three flounder species in the East Sea of Korea using otolith and body morphometric analysis. Reg Stud Mar Sci 93:1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rsma.2025.104714\u003c/span\u003e\u003cspan address=\"10.1016/j.rsma.2025.104714\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMathias M, Veiga-malta T, Storr-paulsen M, Sousa L, Feekings J (2026) Using morphometric relationships for total length prediction of Atlantic cod (\u003cem\u003eGadus morhua\u003c/em\u003e) in electronic monitoring videos. Fish Res 293:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fishres.2025.107633\u003c/span\u003e\u003cspan address=\"10.1016/j.fishres.2025.107633\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehar M (2019) Fish trait preferences:A review of existing knowledge and implications for breeding programmes. Rev Aqua 12:1\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/raq.12382\u003c/span\u003e\u003cspan address=\"10.1111/raq.12382\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelench\u0026oacute;n F, de Mercado E, Pula HJ, Cardenete G, Barroso FG, Fabrikov D, Louren\u0026ccedil;o HM, Pessoa MF, Lagos L, Weththasinghe P, Cort\u0026eacute;s M, Tom\u0026aacute;s-almenar C (2022) Fishmeal dietary replacement up to 50%: A comparative study of two insect meals for Rainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e). Anim 12:1\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ani12020179\u003c/span\u003e\u003cspan address=\"10.3390/ani12020179\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePavlov DA (2015) Condition and health indicators of exploited marine fishes. Mar Bio Res 11:110\u0026ndash;112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17451000.2014.904886\u003c/span\u003e\u003cspan address=\"10.1080/17451000.2014.904886\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedregosa F, Weiss R, Brucher M (2011) Scikit-learn: Machine learning in Python. J Mach Learn Res 12:2825\u0026ndash;2830\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePereira WA, Mendon\u0026ccedil;a CMN, Urquiza AV, Marteinsson V\u0026THORN;, LeBlanc JG, Cotter PD, Villalobos EF, Romero J, Oliveira RPS (2022) Use of probiotic bacteria and bacteriocins as an alternative to antibiotics in aquaculture. Micro 10:1\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/microorganisms10091705\u003c/span\u003e\u003cspan address=\"10.3390/microorganisms10091705\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRandazzo B, Zarantoniello M, Cardinaletti G, Cerri R, Giorgini E, Belloni A, Cont\u0026ograve; M, Tibaldi E, Olivotto I (2021) \u003cem\u003eHermetia illucens\u003c/em\u003e and poultry byproduct meals as alternatives to plant protein sources in gilthead seabream (\u003cem\u003eSparus aurata\u003c/em\u003e) diet: A multidisciplinary study on fish gut status. Anim 11:1\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ani11030677\u003c/span\u003e\u003cspan address=\"10.3390/ani11030677\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobins H, Chapuis L, Kerr CC, Dutka T, Donald J, Collin SP (2025) The inner ear of the Port Jackson shark, (\u003cem\u003eHeterodontus portusjacksoni\u003c/em\u003e): Morphometric analysis using bioimaging and phalloidin staining. Hear Res 466:1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heares.2025.109368\u003c/span\u003e\u003cspan address=\"10.1016/j.heares.2025.109368\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossum GV (2025) The Python Language. The Python Foundation. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.python.org/\u003c/span\u003e\u003cspan address=\"https://www.python.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaleh A, Hasan M, Raadsma HW, Khatkar MS, Jerry DR, Rahimi M (2024) Aquacultural engineering prawn morphometrics and weight estimation from images using deep learning for landmark localization. Aqua Eng 106:1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.aquaeng.2024.102391\u003c/span\u003e\u003cspan address=\"10.1016/j.aquaeng.2024.102391\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva SSDE, Davy FB (1992) Culture systems in Asia. Asian Fish Sci 5:129\u0026ndash;144. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.\u003c/span\u003e\u003cspan address=\"https://doi.org/https://doi.\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eorg/10.33997/j.afs.1992.5.2.001\u003c/span\u003e\u003cspan address=\"10.33997/j.afs.1992.5.2.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrauss RE, Bookstein FL (1982) The truss: Body form reconstructions in morphormetrics. Syst Zoo 31:113\u0026ndash;135. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sysbio.oxfordjournals.org/\u003c/span\u003e\u003cspan address=\"http://sysbio.oxfordjournals.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSutthakiet O, Koonawootrittriron S, Sukhavachana S, Chatchaiphan S (2020) Heritability and genetic correlation of body shape and deformity in snakeskin gourami (\u003cem\u003eTrichopodus pectoralis Regan\u003c/em\u003e, 1910). Aqua 523:1\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.aquaculture.2020.735208\u003c/span\u003e\u003cspan address=\"10.1016/j.aquaculture.2020.735208\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTabassum T, Sofi Uddin Mahamud AGM, Acharjee TK, Hassan R, Akter Snigdha T, Islam T, Alam R, Khoiam MU, Akter F, Azad MR, Al Mahamud MA, Ahmed GU, Rahman T (2021) Probiotic supplementations improve growth, water quality, hematology, gut microbiota and intestinal morphology of Nile tilapia. Aqua Rep 21:1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.aqrep.2021.100972\u003c/span\u003e\u003cspan address=\"10.1016/j.aqrep.2021.100972\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalijan I (2026) Ecological informatics deep learning approach to landmarking and measurement error analysis for gilthead seabream (\u003cem\u003eSparus aurata\u003c/em\u003e) origin classification in geometric morphometrics. Ecol Info 93:1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoinf.2025.103560\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoinf.2025.103560\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerje J, Sistiaga M, Herrmann B (2026) Size limits for the use of Ballan wrasse (\u003cem\u003eLabrus bergylta\u003c/em\u003e) as cleaner fish in Salmon aquaculture cages. Aqua 615:1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.aquaculture.2025.743613\u003c/span\u003e\u003cspan address=\"10.1016/j.aquaculture.2025.743613\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThird GM, Parsons DM (2024) Population identification of Snapper (\u003cem\u003eChrysophrys auratus\u003c/em\u003e) using body geometric morphometrics to inform sustainable fisheries management. Fish Res 280:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fishres.2024.107159\u003c/span\u003e\u003cspan address=\"10.1016/j.fishres.2024.107159\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToledo A, Frizzo L, Signorini M, Bossier P, Arenal A (2019) Impact of probiotics on growth performance and shrimp survival: A meta-analysis. Aqua 500:196\u0026ndash;205. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.aquaculture.2018.10.018\u003c/span\u003e\u003cspan address=\"10.1016/j.aquaculture.2018.10.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTraverso F, Aicardi S, Bozzo M, Zinni M, Amaroli A, Galli L, Candiani S, Vanin S, Ferrando S (2024) New insights into geometric morphometry applied to fish scales for species identification. Anim 14:1\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.3390/ani14071090\u003c/span\u003e\u003cspan address=\"10.3390/ani14071090\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVirtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Walt SJ, Van Der Brett M, Wilson J, Millman KJ (2020) SciPy 1.0: Fundamental algorithms for scientific computing in Python. Natr Methds 17:261\u0026ndash;272. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41592-019-0686-2\u003c/span\u003e\u003cspan address=\"10.1038/s41592-019-0686-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Xie Y, Li Y, Peng F, Li J, Jiang W, Xie B, Fu P (2025) Multivariate and geometric morphometrics reveal morphological variation among Sinibotia fish. Bio 14:1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.3390/biology14091177\u003c/span\u003e\u003cspan address=\"10.3390/biology14091177\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWasso DS, Ayagirwe RBB, Chasinga TB, Ntagereka EM, Mweze AK (2025) Morphometric characteristics, genetic profile, and histology of Nile tilapia in Mwenga territory, South Kivu. Egy J Aqua Res xxx 1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejar.2025.12.001\u003c/span\u003e\u003cspan address=\"10.1016/j.ejar.2025.12.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeththasinghe P, Lagos L, Cort\u0026eacute;s M, Hansen J\u0026Oslash;, \u0026Oslash;verland M (2021) Dietary inclusion of black soldier fly (\u003cem\u003eHermetia Illucens\u003c/em\u003e) larvae meal and paste improved gut health but had minor effects on skin mucus proteome and immune response in Atlantic salmon (\u003cem\u003eSalmo salar\u003c/em\u003e). Front Immuno 12:1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fimmu.2021.599530\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2021.599530\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao B, Wu H, Wei Y (2018) Simple baselines for human pose estimation and tracking. Proceed Eur Conf Comp Vis (ECCV). 1:472\u0026ndash;487. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-01231-1\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-01231-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuur AF, Ieno EN, Smith GM, Landes (2007) Mixed Effects Models and Extensions in Ecology with R. Stat Bio Heal. 4\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-0-387-87458-6\u003c/span\u003e\u003cspan address=\"10.1007/978-0-387-87458-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"body shape, morphometry, inoculation, geometric analysis, landmark, truss network","lastPublishedDoi":"10.21203/rs.3.rs-9672785/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9672785/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFish morphometry (body shape) is a key variable that determines a fish’s ability to feed, swim, reproduce, and perform other biological processes. Morphometric traits indicate fish health and overall well-being, yet their use in determining aquaculture production when probiotics are used in insect diets is not well understood. The relationships of fish morphometric traits with growth parameters have also not been well investigated. The current study describes the performance and relationship of body morphological variations and specific growth trajectories of Redbreasted tilapia fingerlings fed commercial probiotic-inoculated black soldier fly meal diets. The fish were reared for 72 days and fed various probiotic-inoculated diets described as P0\u003csub\u003e \u003c/sub\u003e[0 CFU/kg] as a control\u003csub\u003e, \u003c/sub\u003eP1\u003csub\u003e \u003c/sub\u003e[4×10¹⁰ CFU/kg] as treatment 1, P2\u003csub\u003e \u003c/sub\u003e[8×10¹⁰ CFU/kg] as treatment 2, and P3\u003csub\u003e \u003c/sub\u003e[12×10¹⁰ CFU/kg] as treatment 3. Significantly greater means for the morphometric traits of total length, eye diameter, distance between eyes, operculum length, and pectoral fin length were observed at T2 (p \u0026lt; 0.05). Similarly, the specific growth rate and weight gain were greater in T2, with T0 indicating the highest apparent feed conversion ratio (p \u0026lt; 0.05). Overall, the T2 inoculation level presented the greatest variability, with enhanced morphology-related growth in the present study. The study indicated that probiotic inoculation in BSFL diets enhances morphological variability and fish growth.\u003c/p\u003e","manuscriptTitle":"Probiotic-enriched black soldier fly larvae meal diets impact the relationship between morphometric traits and the growth of Redbreasted tilapia (Coptodon rendalli)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 06:34:31","doi":"10.21203/rs.3.rs-9672785/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc5833df-f7e5-4d5d-93a1-e3f662acf401","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"editorAssigned","content":"","date":"2026-05-11T10:59:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-11T10:58:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Probiotics and Antimicrobial Proteins","date":"2026-05-10T21:08:51+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T06:34:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 06:34:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9672785","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9672785","identity":"rs-9672785","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}