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ABSTRACT
Lake Jipe, a shared resource at the Kenya‐Tanzania border, has experienced significant fish introductions impacting its fish diversity. Despite these changes, the variations in morphometric characters of fish in the lake have not been documented following the establishment of non‐native Oreochromis species. To address this, the present study assessed the morphological differences in Oreochromis niloticus, Oreochromis jipe and Oreochromis esculentus in Lake Jipe using morphometric traits to improve species identification for ease of fisheries management and conservation of the lake. These three species are known to share overlapping traits which makes their differentiation challenging. Using Image Analysis in ImageJ software version 1.54i and statistical analysis using R Version 4.3, the study hypothesized there were no significant differences in the morphometric characteristics of Oreochromis species in the lake. Welch's ANOVA revealed statistically significant correlations (p < 0.05) consistent in all species, with standard length and dorsal having the highest degree of significant positive association with total length, r > 0.8 across species. Correlations were weaker between total length and traits like caudal length (CL) and head length (HL), r < 0.5. All species exhibited negative allometric growth pattern (b < 3) suggesting faster growth in length than weight. This has important implications for fisheries management by guiding size‐based harvesting strategies for sustainability. PCA revealed two principal components, (PC1 = 75.8%, PC2 = 10.4%) which contributed significantly to the observed variance in TL with TW. This study discriminated the species with total body weight and total length being the most distinctive morphometric measurements. Cluster dendrograms displayed site‐specific species grouping with mixed groupings in the dendrograms indicative of potential hybridization events, especially where species distribution overlaps. This underscores the importance of further research to confirm species identity and potential admixture using genetic analyses. By refining the distinguishing traits, this study contributes to improved monitoring and management of Oreochromis fish populations in Lake Jipe.
Introduction
Morphometric analyses are key approaches for identifying population characteristics and elucidating the admixture and hybridization levels of different fish species (Lim et al. 2016; Mwanja et al. 2016; Anam et al. 2021; Tibihika et al. 2019). Recent research has shown that morphometric methods may be used to reveal hybridization in a variety of fish species (Špelić et al. 2021; Perriman et al. 2022). Perriman et al. (2022) investigated the morphological effects of hybridization between farmed and wild Atlantic salmon and observed considerable phenotypic modifications in the hybrids. Similarly, Špelić et al. (2021) used geometric morphometrics to compare the form changes of native, imported and hybrids of Brown trout (Salmo trutta), demonstrating the efficiency of these approaches for detecting hybridization. Therefore, these analyses allow researchers to distinguish subtle differences in body structures across populations by studying the shape and various body dimensions (Mojekwu and Anumudu 2015). In the context of Lake Jipe, morphometric analyses can serve as a useful tool to characterize the fish populations morphologically as well as to detect hybridization events early on by identifying intermediate traits with which researchers can infer potential hybridization, which is crucial for implementing conservation and management strategies to protect native fish populations. In addition, the emergence of advanced computer vision for image processing has been linked to the potential increase in the number, quality and customization of repeated phenotypic assessments (Tuckey et al. 2022). This technology has over time been adopted in fisheries studies as it replaces the subjective human eye with automated processes for the assessment of shapes and lengths (Zion 2012; Costa et al. 2013; Navarro et al. 2016).
Morphological variations among fish populations have been widely studied to identify differences across environments and populations (Endo and Watanabe 2020; Hernandez et al. 2022). Consequently, multi-species fish populations have been observed to be very diverse at the species level, especially in their morphological traits (Brucet et al. 2018; Acar and Kaymak 2023; Ekerette et al. 2024; Stefani et al. 2024). For example, a study on Egyptian fish populations in the Nile and Lake Burullus encompassing species such as Oreochromis aureus, Oreochromis niloticus, Sarotherodon galilaeus and Tilapia zillii revealed significant intra-specific variability in morphometric traits, such as standard length, body depth and head length (Azab et al. 2022). In Lake Jipe, similar traits could be measured and compared among the introduced Oreochromis species and the native Oreochromis jipe which can help strengthen the interpretation of observed patterns in the lake and contextualize the study. Similarly, in Nigeria, Ekerette et al. (2024) employed morphometric and meristic analysis to distinguish between Oreochromis mossambicus, O. aureus and O. niloticus which are three major species that share some morphometric characteristics.
In natural waters, hybridization has been reported as a significant contributor to morphometric variations in fish species (Jafari et al. 2022; Quadroni et al. 2023; Santos et al. 2023). These fluctuations in biodiversity may be tied to the unintended consequences of releasing captive individuals into open water, often leading to genetic erosion of native species through competition, crossbreeding and hybridization (Pallipuram 2020; Santos-Santos et al. 2021). The critically endangered O. jipe in Lake Jipe is of particular concern due to its gradually declining population (Orina et al. 2024). The decline of O. jipe in Lake Jipe is driven by multiple factors, including overfishing, habitat degradation and the presence of invasive species such as O. niloticus and Oreochromis esculentus. Hybridization with these invasive species can exacerbate the problem by leading to genetic dilution, reducing the reproductive success of the native species and further weakening the resilience of the population in the face of environmental pressures (Orina et al. 2024). While specific hybrid species in the lake have not been documented fully, preliminary research observations suggest that there is potential hybridization between the Oreochromis species in Lake Jipe resulting in altered morphometric features and genetic diversity (Mnaya et al. 2021; Orina et al. 2024). This necessitates the need for further research into the presence and distribution of hybrid O. jipe as this would provide insights into the implications of hybridization on the lake's biodiversity.
Lake Jipe, a shallow lake (approximately 1 m average depth at present), was originally about 100 km2 but presently spans approximately 30 km2 due to heavy siltation (Ndetei 2011; Orina et al. 2024). The lake has long been used by the indigenous populations in the two bordering countries of Kenya and Tanzania to sustain artisanal fishing (Froese and Pauly 2018). The African catfish (Clarias gariepinus), Jipe tilapia (O. jipe), Labeo spp. and Barbus spp. are the widely recognized native species of Lake Jipe that have historically been significant to the economy of the local communities (Mshana 2016). As the lake fisheries gradually declined, Tilapia singida (O. esculentus) was slowly introduced into the lake which later led to the displacement of O. jipe (Mshana 2016). To boost the fast-declining lake fisheries, the County Government of Taita Taveta and the National Government Fisheries Department of Tanzania introduced O. niloticus in 2015 (Omweno et al. 2023). However, O. niloticus increasingly became aggressive over the years leading to a further decline in stocks of the native O. jipe substantially affecting the fisheries of this commercially significant endemic species of the lake (Orina et al. 2023, 2024). While hybridization may have been a factor that contributed to the displacement of O. jipe, its displacement is also likely to have resulted from the interplay of ecological competition and other environmental factors which created favourable conditions for the invasive species, O. esculentus and O. niloticus being more adaptable species. Recent studies suggest that O. jipe is only found distributed in a small range of area of the Pangani catchment spanning less than 100 km2 (Genner et al. 2018; Shechonge et al. 2018; Omweno et al. 2020). The Pangani catchment comprising Lake Jipe, the Pangani river tributaries and the downstream Nyumba ya Mungu Reservoir has been extensively overfished to the extent that O. jipe is currently listed in the International Union for Conservation of Nature (IUCN) Red List of Critically Endangered Species (Froese and Pauly 2018). The effects of irrigation activities upstream coupled with an increase in Typha domingensis area coverage around the lake have resulted in the receding water levels and decline of fish stocks (Mwachiro et al. 2012: Mnaya et al. 2021). This has resulted in the massive loss of nursery grounds for Oreochromis species in addition to the sustained overfishing and exotic fish species competition against O. jipe (Angienda et al. 2011; Blackwell et al. 2020; Kariuki et al. 2021). As a result, its population structure has been greatly compromised.
In natural waters, the fish morphometric assessment remains the simplest method used in the differentiation and classification of tilapia fish for use in breeding improvement (Ikpeme et al. 2017; Mwanja et al. 2016; Bradbeer et al. 2019; Makeche et al. 2020; Kwikiriza et al. 2023). Despite the growing concerns arising on the introduction of non-native Oreochromis species in Lake Jipe (Deines et al. 2014; Bradbeer et al. 2019; Omweno et al. 2020), there is limited information on the morphometric variations of these species in Lake Jipe. As a result, the limited understanding of the lake's fishery has contributed to the general lack of proper management and conservation scheme for the lake's native fish resource. Therefore, to inform species-specific management and conservation plans for the endangered O. jipe, this study assessed the morphometric variations of Oreochromis species in Lake Jipe.
Materials and Methods
Study Area
The study, which was a one-time sampling event, was conducted in Lake Jipe in Taita Taveta County in March 2024 to capture a snapshot of the current phenotypic diversity in Lake Jipe and can be helpful in identifying distinguishing traits among the species. The lake is an inter-territorial fisheries resource at the Kenya-Tanzania border and lies between latitude S03°36′15.96ʺ and longitude E37°51′51.12ʺ on the Kenyan side and latitude S03°31′43.00ʺ and longitude E37°51′59.04ʺ on the Tanzanian side (Figure S1). The lake has one primary inlet, the River Lumi, which originates from Mt. Kilimanjaro and an outlet, the River Ruvu, which drains water into Nyumba ya Mungu reservoir in Tanzania. The lake also receives water from the Pare Mountains via River Mvulani and several other temporary streams from the mountain ranges. The lake is a shallow basin, with an approximate average depth of 3 m, 19 km in length and an estimated surface area of 30 km2 (Ndetei 2011).
Fish Sample Collection
A total of 167 samples were collected from three sampling sites. The three major sampling locations of Lake Jipe are as follows: Kachero (n = 58) (S03°35′15.1ʺ, E037°46′15ʺ), Kenya Wildlife Service (KWS) (n = 59) (S03°37′49ʺ, E37°46′42ʺ) and Mbugani (n = 50) (S03°37′29ʺ, E37°45′59ʺ). The GPS coordinates reported represent the target sampling points that were pre-determined based on prior knowledge of the distribution of the target species in the lake. The sampling locations were recorded in a smartphone and rechecked for consistency, accuracy and verified using Google Earth. The presentation is in Degrees, Minutes and Seconds (DMS). The study focused on three major species of the genus Oreochromis; O. jipe, O. esculentus and O. niloticus which were collected from each site for assessment. Images of each fish were recorded for measurement of various morphometric characteristics. The fish samples were identified to the species level using standard taxonomic manuals (Trewavas 1983) for Oreochromis identification.
Morphometric Characters
The morphometric measurements were done on the left side of the body of the fish. The total length of the fish was measured and recorded with an accuracy of ± 0.1 cm and total body weight was measured to the nearest 0.1 g. The morphometric parameters measured were: total length (TL), standard length (SL), dorsal fin length (DL), caudal fin length (CL), head length (HL), body width (BW), pectoral fin length (PEC) and pelvic fin length (PEL). Image analysis was done using ImageJ software version 1.54i with the scale calibrated to the nearest 0.1 cm. Fish body weight was measured on a weighing balance after dabbing off water from the fish body using blotting paper. All morphometric characters were measured and recorded in centimetres with respect to the TL and weight in grams.
Data Analysis
Microsoft Excel 2021 was used for all data entry and data management while data on all morphometric measurements from the study were subjected to descriptive statistics using R (Version 4.3.1), both of which are widely accepted software for morphometric analyses.
The computation of the means, standard deviations (SD) and range was done using the raw data and fish lengths and weight presented as mean ± SD. Normality test using the Shapiro–Wilk test was conducted at a 5% significance level and data was standardized using the Z-Score prior to correlation and regression analysis, principal component analysis (PCA) and hierarchical cluster analysis to ensure accurate comparisons of morphometric traits across the species and study sites.
Homogeneity of variance was checked using Levene's test and data did not meet all assumptions of the traditional One- and Two-way ANOVA, meaning that the assumption of homogeneity of variance was violated (p < 0.05). To account for violations of the assumptions of homogeneity of variance, Welch's one-way ANOVA was adopted, as this method is robust to unequal variances in a dataset. This was performed at a 5% level of significance to assess the differences in the means of the morphometric variables between and within the species under study. Welch's two-way ANOVA was used to test for main effects and any interaction effects between the three populations of fish from the three sites and the species. Further Post Hoc tests were carried out to ascertain the sites and species that differed.
Calculation of correlation coefficients (r) and regression lines was done using the least squares method. The coefficient of determination (R2) was computed to determine the quality of relationship between all variables and TL. Regression analysis was conducted to assess the degree to which the independent variables influenced the dependent variable with the aim of making predictions and evaluating the model's performance and the coefficients’ stability. Key assumptions of regression such as normality, homoscedasticity and multicollinearity were assessed to ensure model validity. Multicollinearity was assessed to ensure that standard errors of the estimates are narrow to improve the precision and stability of the estimates.
To test multicollinearity, a variance inflation factor (VIF) was adopted for which the general rule of interpretation of a VIF value more than or equal to ten (≥ 10) implies a multicollinearity issue (Oke et al. 2019). One of the approaches applied to take care of the multicollinearity and have the estimates more consistent was the use of ridge regression which handles multicollinearity through the introduction of a penalty term (λ) by adding a small positive constant to the variables before calculating the coefficient estimates. This shrinks the regression coefficients of highly correlated variables and this may present a potential limitation of the model as it introduces a bias to reduce the variance. The ridge regression equation fitted was:
Key outputs from the Ridge Regression were the regression coefficients which infer the relationship between the predictor variables and TL as the response variable, and the R-squared and root mean square error (RMSE) metrics which evaluate model performance.
PCA of all traits was conducted by employing multivariate analysis on all the scaled data to determine how much the morphometric characters contributed to the observed variability.
The relationship between fish TL and total weight was assessed in a linear regression using the equation, W = aLb, following Le Cren (1951), where: W = Body weight of fish (g), L = TL of fish (cm), a = Intercept, and b = slope.
The data was visualized using R plots and the results of the study were presented in tables and box plots. In all analyses, fish TL was taken as the dependent variable and all the other parameters including weight considered independent variables.
Software Validation
The software used in the present study are very reliable and the findings of the study are robust and can be reproduced. R Software was validated using a publicly available Iris dataset to ensure the statistical packages used work as intended and to verify the accuracy of results against known results. ImageJ was validated through calibration of the scale measurements with a 30 cm ruler as the known reference scale and testing its accuracy using sample images prior to analysing the actual samples.
Results
Comparison of Morphometric Traits Across Species and Sites
Welch's Analysis of variance revealed that TL, TW, SL, DL, HL, CL, BW, PEL and PEC were statistically different at p < 0.05 between the species while no significant differences were recorded within the species (p > 0.05). Two-way analysis of variance showed significant differences in the present dataset in which both species and site had a significant effect on the observed variations in length parameters in the fish under study. Further assessment using a post-hoc test was conducted to determine which sites and species differed from each other in terms of their morphometric characteristics.
Generally, the Oreochromis species exhibited variations among the morphometric traits with the mean TL being higher in O. esculentus (17.0 ± 2.59 cm) followed by O. jipe (16.5 ± 1.22 cm) while O. esculentus exhibited the lowest TL (14.8 ± 2.78 cm) (Table S1). The average body weight was higher in O. niloticus (48.5 ± 35.67 g) followed by O. jipe (28.1 ± 16.34 g) while O. esculentus had the lowest value (23.5 ± 8.73 g) (Table S1). Post-hoc analysis revealed no significant difference in the total weight of O. jipe and O. esculentus. The respective distribution for the TLs and total weights for the respective species are shown in Figure S2.
Comparative analysis of TW (g) and TL (cm) across the three study sites also revealed significant variations (p < 0.05). The spread and central tendencies of these differences are presented in Figure S3. A greater variability was observed in both the TL and total weight of O. niloticus across all the sites when compared to the O. esculentus and O. jipe species.
Exploring the Relationship Between Morphometric Characters for the Oreochromis Species
The Pearson correlation coefficient (r) revealed a positively high significant correlation (r > 0.70) between parameters (SL, DL, PEL) and TL in O. jipe, (SL, DL) and TL in O. niloticus, while all variables were positively highly correlated with TL in O. esculentus (Table S2). Both O. esculentus and O. jipe had high VIF values exceeding 10 in a number of their variables while O. niloticus had relatively low collinearity. Regression coefficients derived for all the predictor traits varied in strength and direction from one species to the other highlighting how the independent variables influenced the response variable (Table S3). The fitness of the model was exceptionally high as seen by the R2 values. In all the species, the model was able to explain over 98% of the variance in the response which is a very high estimation (Table S3). RSME were relatively low demonstrating that the model had a very high predictive accuracy.
Length-Weight Relationship Among the Species
The relationship between the fish's TL and total weight was assessed using the equation, W = aLb, following (Le Cren, 1951) to determine the growth pattern of the fish studied. The ‘b’ values of the fish samples ranged from a minimum of 1.98 to a maximum of 2.18, with a mean value of 2.07 (Table S4). All the species showed negative allometric growth (b < 3) with ‘b’ values of the regression slope being 2.18, 2.06 and 1.98 for O. niloticus, O. jipe and O. esculentus respectively (Table S4).
Principal Component Analysis
Analysis of principal components identifies the principal components based on their eigenvalues which are a direct reflection of the amount of variance each principal component explains in the dataset. The analysis of the morphometric variables produced two significant principal components, PC1 (75.8%) and PC2 (10.4%) (Figure S4). PC1 and PC2 were selected because together they explained the largest portion of the variance in the study (> 80%). Both components were also retained as they had eigenvalues greater than 1 which ensured that the analysis focused on key traits driving variability. This means that they are able to capture the most meaningful patterns in the data and help distinguish species as per their morphometric traits. The most influential variable for principal component 1 was total weight while caudal length was significant in PC2. Both total weight and caudal length capture very key functional characteristics and biological traits of fish. The total weight provides a very concise measurement that reflects the overall fish size, growth condition and health of the fish. The existence of variations in caudal length may be indicative of the different ecological niches to which the fish are adapted. The O. jipe appeared to have a very dispersed but distinct pattern of distribution indicating greater variability in its traits while O. niloticus and O. esculentus overlapped and were evenly distributed throughout the lake as visualized in the PCA biplot of the present study potentially giving evidence of some shared morphological traits between the two species (Figure S4).
Species-Specific Hierarchical Cluster Analysis
Hierarchical cluster analysis revealed observable patterns of morphometric variations across the sampled sites. All three sites displayed two distinct clusters (Figures S5–S8). O. niloticus from Mbugani and KWS sites formed two major clusters while a mix of individuals from both Kachero, KWS and Mbugani formed the second cluster indicative of an overlap of morphometric characteristics across the three sites (Figure S6). The dendrogram for O. esculentus also displayed two distinct clusters with the first cluster being dominated by individuals from KWS and the second distinct cluster primarily dominated by individuals from Kachero with registered overlap with those from KWS (Figure S7). O. jipe dendrogram displayed two primary clusters with individuals mixed from all the sites creating a lack of distinct separation reflecting less site-specific differentiation (Figure S8) when compared to O. niloticus and O. esculentus.
Discussion
Morphometric studies are essential in tilapia fisheries management as they help in species identification, growth monitoring and environmental assessment (Nyingi et al. 2021; Kwikiriza et al. 2023; Ramírez-Coronel et al. 2024). By characterizing the morphometric traits of fish species, these studies aid in distinguishing tilapia species, preventing misidentification and hybridization, and ensuring effective conservation. Variations in the morphometric characters in fish have been studied widely (Hanif et al. 2019; Basuonie et al. 2020; Moreira et al. 2020; Masood et al. 2024). Collectively, these studies demonstrate the complex interplay of genetic, environmental and ecological factors that affect fish morphometric characteristics.
In the current study, species discrimination was highly characterised with the total body weight and fish TL giving the most distinctive values. These differences made it possible to clearly distinguish between the three Oreochromis species in Lake Jipe. The substantial differences recorded between the species are suggestive of size variations among the three fish species (Asmamaw and Tessema 2021). This study hypothesized no significant differences in the morphometric parameters among the populations of Oreochromis species from Lake Jipe. The significant effect on site on the length parameters implies location-based factors such as the water quality, food availability and also varying habitat conditions that may influence the length parameters.
Fish conservation strategies must consider the importance of the interlink between morphological features. The records of high correlations are indicative of consistent growth relationships that can easily be monitored to help detect early signs of ecological stress or degradation in Lake Jipe, guiding timely conservation interventions to protect these species and their habitats. The use of ridge regression as a more regularized regression method has been used for a long time in datasets with high collinearity as it reduces the chances of overestimation of coefficients, thereby making predictive models more robust and informative (Schreiber-Gregory 2018). From the results in Table S3, the data implies that selected traits significantly influence fish TL. For instance, SL had relatively high positive coefficients across the species which implies that it plays a very dominant role in the prediction of TL in all the species. The occasional negative and very small coefficients for variables like BW imply that these traits play a less significant role in TL prediction using the regression model and also that some parameters are inversely correlated in some species as seen by the negative coefficients. The use of ridge regression in the present study provided more accurate insights into the contributions of the morphometric traits to the overall fish growth. Understanding these contributions is important for fisheries management, particularly in an effort to design growth prediction models for conservation strategies for endangered species like O. jipe and O. esculentus (Maithya 2010). Further research may however benefit from the inclusion of other environmental factors to improve model precision and wider applicability.
The connection between all the morphological features and their contribution to observed variability was evaluated by PCA. Although the analysis was done on all traits, reporting only considered TL and TW thus excluding the other characters. The exclusion was solely to focus on the traits that had the strongest correlations and significantly contributed to explaining the observed variance. The first two principal components accounted for over 80% of the total variance. These were interpreted to explain the variance in the TL and total weight which were the key traits contributing to species morphometric differentiation. This limit also ensured that most of the variance in the dataset was captured while at the same time minimizing the effects of dimensionality. The high variability in O. jipe and the unique properties of O. niloticus highlight significant intraspecific diversity and distinct morphometric profiles, respectively, that are easily identifiable for management in fisheries.
Assessment of the length-weight relationship using a simple regression revealed that all species from Lake Jipe displayed a negative allometric pattern of growth which means that the fish's total weight increased disproportionately in relation to the fish's TL. The fish's length increased faster than the rate at which the fish added weight as it grew. This finding is consistent with Ngodhe and Okeyo Owuor (2019) who found that wild populations of O. niloticus in Lake Victoria exhibited negative allometric growth, attributing it to ecological influences and genetic contributions. In the context of Lake Jipe, the observed negative allometric growth pattern in all the Oreochromis species could be attributed to several local factors. Environmental stressors such as fluctuating water levels, habitat degradation and pollution may limit the energy available for fish to grow in proportion to their size. Furthermore, food availability in the lake may not be sufficient to support rapid weight gain, particularly in times of resource scarcity. The competition with invasive species, especially O. niloticus, for limited food sources may also lead to reduced growth rates. The implication of negative allometric growth is a reduction in the overall biomass of the lake's fishery thus very low economic returns to the fisher-folk of Lake Jipe. This makes these species highly vulnerable to overfishing as evidenced by the presently low population of O. jipe in the lake (Orina et al. 2024). Therefore, fishery management strategies must adjust size limits and harvest quotas to ensure sustainable biomass in Lake Jipe.
The most distinct site-specific clustering of O. niloticus, especially between Mbugani and Kachero, suggests that both environmental, fish introductions and genetic factors may be driving morphometric divergence between species from the two sites. Introductions of tilapia species in East African water bodies date to the 1950s and 1960s (Shechonge et al. 2019). Ideally, this period isn't long enough to allow morphological differentiation among the different populations of O. niloticus. Similarly, the pattern of distribution in the O. esculentus dendrogram suggests that while site-specific morphometric traits exist, some level of trait similarity from the different sites can still be observed. Contrary to these findings, O. jipe displayed more mixed clustering among the different sites. This clustering could most probably be a result of unintentional fish transfers from one landing site to another, especially by the fishermen. Similar cases have been reported to contribute to the close clustering of O. niloticus in Lake Victoria and attributed this to unintentional transfer of fish by fishermen to different beaches (Tibihika et al. 2020; Kariuki et al. 2021; Kwikiriza, et al. 2024). The overlaps in the O. jipe dendrogram could indicate a high possibility of these fish having a greater gene flow thus high genetic similarity, occurrence in similar environmental conditions or homogeneity of morphometric traits arising from constant movement between the sites. This even distribution of clusters suggests that O. jipe from the two sites are not strongly differentiated thereby suggestive of very close genetic distances and similarities as a result of having a common ancestry.
These results highlight valuable implications for site and species-specific management strategies for Lake Jipe. From the results, since O. jipe did not group together with any of the introduced species, O. niloticus and O. esculentus, this suggests that O. jipe has maintained its genetic uniqueness thus its distinct morphometric traits. This clear separation is important for conservation efforts in the lake being that O. jipe is presently a critically endangered species. These results highlight the need for channelled efforts to protect O. jipe's native habitat to maintain the ecological balance between introduced and native species.
Conclusion
The significant differences that existed in body length parameters among the three species are likely reflective of the environmental and genetic influences on the species’ phenotypic traits. Despite the limitations in species availability in the study sites hindering concrete site comparisons, the study has integrity and these findings provide insights into the morphometric dynamics of the genus Oreochromis in Lake Jipe. The application of molecular methods can be useful in investigating the discreteness of Oreochromis species in Lake Jipe to provide insights into the genetic diversity and population structure of these species. Therefore, future research should take up molecular studies to elucidate the significance of genetics and the environment and their combination in influencing the morphology of fish in Lake Jipe.
Author Contributions
Elizabeth Nyauchi: conceptualization, investigation, writing – original draft, methodology, visualization, writing – review and editing, formal analysis, data curation, software. Gerald Kwikiriza: writing – review and editing, supervision, methodology. Harald Meimberg: writing – review and editing, validation, supervision. Geoffrey Ong'ondo: writing – review and editing, validation, supervision.
Acknowledgements
The authors are grateful for the funding support from The Rotary Club of Vienna and to Professor Nzula Kitaka for her continuous support and encouragement. The authors are also grateful to Adnice Atemo for helping with the sample collection and taking of measurements in the field.
Conflicts of Interest
The authors declare no conflicts of interest.
Data Availability Statement
The data used to support the findings of this study are available from the corresponding author upon request.
Peer Review
The peer review history for this article is available at .
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