Morphometric multivariate analyses reveal population structuring of Pennahia aneus (Perciformes: Sciaenidae) in Malaysian waters

Morphometric multivariate analyses reveal population structuring of Pennahia aneus (Perciformes: Sciaenidae) in Malaysian waters | 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 Article Morphometric multivariate analyses reveal population structuring of Pennahia aneus (Perciformes: Sciaenidae) in Malaysian waters Jolly Babangida Kachi, Nur Wadhihah Hashim, Khaled Binashikhbubkr, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7209099/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Pennahia aneus is a commercially important demersal sciaenid fish found in the Indo-Pacific region, yet its population structure in Malaysia remains insufficiently documented. This study aimed to differentiate four populations of P. aneus in Peninsular Malaysia and Borneo using morphometric multivariate analyses. A total of 423 samples were obtained from 22 landing sites across the South China Sea, Celebes Sea, Sulu Sea, and Strait of Malacca. Eleven morphometric characteristics were measured and used for the analyses. Principal component analysis (PCA) revealed minimal variability in body shape and size due to significant overlap among groups. Hierarchical cluster analysis (CA) identified two distinct morphological groups: the South China Sea population and the Strait of Malacca-Celebes-Sulu Sea population. This classification was reinforced by discriminant function analysis (DFA) and uniform manifold approximation projection (UMAP). The most distinctive morphometric traits for differentiating P. aneus populations were eye depth (ED), snout length (SnL), caudal peduncle depth (CPD), and head length (HL). The classification accuracy for these populations was 71.65%. This study provides the first comprehensive insight into the structured population of P. aneus in Malaysia, offering valuable information for conservation and sustainable resource management. However, molecular validation is necessary to refine the population structure further and strengthen effective management strategies. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Ocean sciences Biological sciences/Zoology morphometric differentiation Pennahia aneus population structure stock assessment sustainable fisheries Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Malaysia, situated in the Sundaland biodiversity hotspot, is renowned for its significant diversity and endemism [1,2]. It is one of the 17 megadiverse countries worldwide, as identified by Conservation International (CI) for meeting the main criterion of hosting at least 5,000 endemic species [3]. Malaysia's various ecosystems, including tropical rainforests, mangroves, and coastal habitats, support a wide range of flora and fauna, contributing to its ecological importance at a global scale [4]. Its freshwater and marine ecosystems are crucial for fishery resources. Yet, they are threatened by unsustainable fishing methods, habitat destruction, and climate change, which demand conservation-driven policies and sustainable fisheries management [3,5]. Recently, the Malaysian marine fisheries industry has gained significant attention because of its potential to be reckoned with as one of Southeast Asia’s prominent fishing hubs [6,7]. This is particularly due to Malaysia’s strategic positioning among vital marine ecosystems, which affirms its maritime and rich marine biodiversity status. The important seas bordering Malaysia include the South China Sea, which is situated to the east of Peninsular Malaysia and west of East Malaysia (Sarawak and Sabah); the Strait of Malacca, which is located west of Peninsular Malaysia, between the islands of Sumatra (Indonesia) and Malaysia; the Sulu Sea, which is located northeast of East Malaysia (Sabah); the Celebes Sea, which is situated east of East Malaysia (Sabah), and the Andaman Sea, which is located northwest of Peninsular Malaysia [8]. The Malaysian marine fishing industry plays a crucial role in the national economy by meeting protein needs, creating job opportunities, and facilitating exports to neighbouring countries [9,10]. The annual statistics by the Department of Fisheries, Malaysia, indicated that 1,308,415 metric tonnes of seafood, valued at RM 11.3 billion, were provided in 2022, whereas 1,220,277 metric tonnes, valued at RM 11.44 billion, were contributed in 2023 [11]. Pennahia aneus , is a medium-sized, commercially important demersal fish belonging to the Sciaenidae family, and widely distributed in the Indo-West Pacific region [12], including Malaysia. According to Lim et al. [13] P. aneus and four other species of the genus Pennahia , collectively known as Pennah croakers, constitute a crucial part of the coastal ichthyofauna essential for subsistence fisheries in Southeast Asian nations. It is suitably marketed fresh, salty, or dried due to its high economic value and delicate flavour [14]. Despite being classified as a species of least concern (LC) by the International Union for Conservation of Nature (IUCN), P. aneus encounters notable threats like overfishing, especially as bycatch in commercial fisheries, along with the adverse effects of pollution and habitat degradation, which collectively endanger its population and the ecosystems it occupies [15]. According to Third & Parsons [16], overfishing reduces marine biodiversity and threatens ecosystem stability by depleting vulnerable populations and undermining intraspecific diversity, which is essential for resilience against stressors. The morphometric study of fish is vital for fisheries management, enabling the assessment of evolutionary traits and the detection of changes in morphology, ontogeny, function, and evolutionary connections among individual samples [17,18]. Inadequate consideration of stock structural components in fisheries management plans can lead to population deterioration due to biological changes and decreased productivity rates [19]. Fish species identification relies primarily on external morphological characteristics, including body form, colour patterns, scale size, the number of fins, fin rays, fin spines, and proportional measurements of various body sections, which are used as distinguishing markers to differentiate between species effectively [9,20]. Morphometric studies are cost-effective and straightforward methods for identifying and characterizing fish stocks [21], establishing the layout of fish assortment [22], and distinguishing within or between closely related fish populations [23-25]. Evaluating the morphometric differences of fish is a preliminary method for delineating various stocks [19,26]. Multivariate techniques, including cluster analysis (CA), principal component analysis (PCA), canonical variate analysis (CVA), and discriminant function analysis (DFA), are commonly used to differentiate between populations of the same or different fish species, with numerous studies utilizing these methods to evaluate morphometric variations [7,20,27-31]. Reports on the stock structure of P. aneus , using morphometric variations in Malaysian waters, including Borneo, are scarce. Hamid et al. [32] focused only on the distribution and diversity of fish species in the urbanized areas of the Penang Strait, revealing P. aneus as the third most abundant fish species. However, geometric morphometrics have been used to uncover morphological variations in P. aneus from northern Peninsular Malaysia [33]. All Sciaenids, including P. aneus , are generally identified as ‘gelama’ by the Department of Fisheries, Malaysia, and their total catch at landing sites is rarely differentiated into specific species. This constitutes difficulties for species identification, and the absence of taxonomic knowledge to address this issue could challenge the formulation of effective management strategies for species facing the threat of decline [6]. To address this gap, this study employed multivariate morphometric techniques to delineate four distinct populations of P. aneus from Peninsular Malaysia and Borneo (Sabah and Sarawak) for the first time, with the expectation of discovering whether unique morphological groups exist that could be considered ideal conservation units for sustainable utilization. The results could also provide essential baseline information on the stock structure of P. aneus in Malaysian waters. Table 1. Description of sampling locations and size. Note: SM-Straits of Malacca, SCS-South China Sea, SS-Sulu Sea, and CS-Celebes Sea No. Sampling Points Coordinates Marine Region Size (N) 1 Kuala Kedah 6.1072⁰ N; 100.2953⁰ E SM 32 2 Kota Kuala Muda, Kedah 5.5833⁰ N; 100.3833⁰ E SM 14 3 Kuala Muda Penaga, Penang 5.5759⁰ N; 100.3411⁰ E SM 14 4 Batu Maung, Penang 5.2834⁰ N; 100.2834⁰ E SM 27 5 Kuala Kurau, Perak 5.0150⁰ N; 100.4306⁰ E SM 12 6 Pantai Remis, Perak 4.4580⁰ N; 100.6295⁰ E SM 10 7 Kuala Selangor 3.3500⁰ N; 101.2500⁰ E SM 14 8 Pantai Teluk Kemang, Negeri Sembilan 2.4538⁰ N; 101.8553⁰ E SM 20 9 Pasir Panjang, Negeri Sembilan 2.4253⁰ N; 101.9277⁰ E SM 20 10 Kuala Sungai Baru, Melaka 2.3665⁰ N; 102.0122⁰ E SM 20 11 Tanjong Kling, Melaka 2.2173⁰ N; 102.1622⁰ E SM 20 12 Sematan, Sarawak 1.8064⁰ N; 109.7798⁰ E SCS 20 13 Kuching, Sarawak 1.6057⁰ N; 110.4381⁰ E SCS 20 14 Mukah, Sarawak 2.8974⁰ N; 112.0960⁰ E SCS 20 15 Bintulu, Sarawak 3.2408⁰ N; 113.0620⁰ E SCS 20 16 Miri, Sarawak 4.3990⁰ N; 113.9835⁰ E SCS 20 17 Labuan, Sabah 5.2767⁰ N; 115.2472⁰ E SCS 20 18 Kota Kinabalu, Sabah 6.1254⁰ N; 116.1317⁰ E SCS 20 19 Kudat, Sabah 6.8805⁰ N; 116.8460⁰ E SS 20 20 Sandakan, Sabah 5.8344⁰ N; 118.0883⁰ E SS 20 21 Lahad Datu, Sabah 5.0183⁰ N; 118.3238⁰ E CS 20 22 Tawau, Sabah 4.2425⁰ N; 117.8840⁰ E CS 20 Total 423 Results Principal component analysis (PCA) The PCA of the morphometrics produced ten principal components (PCs) to analyse the body shape and size differences among the four P. aneus populations (Fig. 3a). The first principal component (PC1) was the most important with an eigenvalue of 4.53 and a 45.14% variance (Fig.3b). The second principal component (PC2) also had an eigenvalue of 2.01 and a variance of 20.10%. Notably, the major morphometrics accounting for 65.24% of the observed total variations are captured by combining PC1 and PC2 (Fig. 3b), the most important principal components. The scatter plot of PC1 and PC2 (Fig. 3b) shows partial overlap among populations from the Strait of Malacca, Sulu, and Celebes, indicating restricted variation in body size and shape across these marine regions. This suggests a general similarity in morphometric traits among these populations. However, P. aneus populations from the South China Sea formed a relatively distinct grouping along PC1. This separation highlights a measurable difference in morphometric characteristics from the other populations. Cluster analysis (CA) Despite the morphological overlap observed in the PCA, hierarchical cluster analysis (CA) based on Mahalanobis distances identified two distinct morphological clusters among P. aneus populations (Fig. 4). The first cluster exclusively comprised populations from the South China Sea, reflecting a degree of morphological separation from other regional populations. The second cluster consisted of populations from the Straits of Malacca, with overlapping populations from the Sulu and Celebes Seas. This grouping indicates a closer morphological affinity among populations within these regions, consistent with the partial overlap observed in PCA. The clustering pattern suggests underlying morphological similarities among these populations, reinforcing the notion of shared morphometric characteristics across connected marine environments. Uniform manifold approximation and projection (UMAP) Further visualization of the UMAP (Fig. 5) revealed two broad morphometric clusters, primarily in the South China Sea and other maritime regions, particularly the Strait of Malacca. The significant separation along UMAP 1 indicates different structuring in morphometrics along the east and west coasts of Peninsular Malaysia, requiring further molecular testing to determine whether the cause is environmental adaptation or potential gene flow. A partial separation along UMAP 1 is revealed of the Celebes and Sulu Sea smaller groups, which overlap with the main groups. Along UMAP 2, there is no clear vertical separation, especially between the South China Sea and the Malacca Strait. However, it likely captures intra-group variations, which is suggestive of population sub-structuring as observed by the smaller grouped clusters near the plot origin with an approximate value of UMAP 2 = 9. The compactness and distinctiveness of this group indicate the existence of a cryptic morphometric population that is not geographically defined. The RF analyzed morphometric features to distinguish P. aneus populations. Eye diameter (ED) was the most important trait, followed by snout length (SnL), caudal peduncle length (CPD), and total length (TL). These features are essential for classifying populations and understanding how the species adapts to its environment. However, the distance between the first dorsal fin (DD) and the body depth (BD) had the least influence on defining morphological groups. The RF analysis demonstrated that these features are not all equally important for differentiating groups, emphasizing the power of RF analysis in identifying primary morphological factors. DFA revealed morphometric variations between the groups. Three canonical discriminant functions were identified and used for the discriminant function analysis; however, only the first two discriminant functions effectively combined to explain 99.7% of the total morphometric variation. Only the first discriminant function accounted for the variations with a percentage variance of 96.09%. In contrast, the second discriminant function had a percentage variance of 3.61% (Fig. 7). The DFA scatter plot revealed that the four population group centroids were discriminated into two groups. Specifically, one morphometric group encompassed the South China Sea population, and the other group comprised the Straits of Malacca populations with overlapping populations from the Celebes and Sulu Seas (Fig. 7). Interestingly, the DFA corroborated the PCA, CA, and UMAP analysis by revealing two morphometric groups of P. aneus in Peninsular Malaysia and Borneo. Assessment of Classification Accuracy The Discriminant function analysis (DFA) model, using a 30% holdout set of the data, was used to evaluate the classification accuracy for P. aneus populations, with the results summarized in the confusion matrix (See supplementary Table S1). The overall classification accuracy was 71.65% , representing the proportion of fish samples correctly assigned to their respective groups. The South China Sea population showed 88.09% classification accuracy , with 11.91% being misclassified as the Malacca Strait group. This finding aligns with the PCA and CA results, which consistently highlighted the uniqueness of this population. Similarly, the Strait of Malacca population presented a high classification accuracy of 88.52% , reinforcing its relatively distinct morphometric characteristics while maintaining some overlap with adjacent populations. Conversely, the Celebes and Sulu Sea populations had a 0% classification accuracy , indicating substantial morphological similarity with the Malacca Strait population. The absence of accurately classified samples from the Sulu and Celebes Sea population suggests a significant overlap in body size and shape morphometrics with neighbouring regions, reflecting a gradient of morphological traits rather than clear-cut separation. These DFA results strongly affirm the findings from PCA and CA, collectively demonstrating that P. aneus populations exhibited some degree of morphological differentiation. Discussion The use of morphometric multivariate analyses to investigate the stock structure of P. aneus across Malaysian waters in this study was successful, as two morphometric groups were revealed. The findings of this study confirm the reliability of multivariate analytic techniques in identifying fish stocks as previously reported [7,29,33,34]. Morphological variation is often observed within and among populations due to the segregation of a population within its native habitat. The within-group variance in morphometrics explained by PC1 and PC2 (Fig. 3b) reinforces the findings from past studies where the initial principal components captured the most morphometric differences. Specifically, the combined effect of PC1 and PC2 accounted for 65.25% of the total variance in this study, highlighting their significant role in explaining morphological variability across P. aneus populations. This result is consistent with previous studies examining morphometric differentiation in marine fish species. Asuquo & Ifon [35] reported that PC1 and PC2 explained 96.9% of the total variance in the morphometrics of Pseudotolithus elongatus from the Nigerian Cross River estuary. Similarly, Hanif et al. [36] reported that PC1 and PC2 collectively contributed 70.11% of the total variance in their study of morphometric variability in Amblygaster clupeoides (Sardines) from the Bay of Bengal Coast of Bangladesh. However, few studies have reported slightly different patterns of principal component contributions. For example, Kachi et al. [33] reported that the first four principal components together explained 75.86% of the variance in P. aneus populations from northern Peninsular Malaysia, suggesting a more distributed morphometric influence across multiple components. Similarly, Imtiaz & Md Naim [37] and Binashikhbubkr et al. [7] showed that the first four principal components accounted for 80% of the body shape variation in Nemipterus species and 96.42% of the shape variation in Euthynnus affinis within Malaysian waters. These findings highlight variations in the extent to which individual principal components contribute to morphometric differentiation across different fish species and regions. Despite these differences, the consistently high variance explained by principal components in various studies underscores the effectiveness of PCA in capturing morphological distinctions, reinforcing its robustness as a tool for assessing population structure. The PCA results revealed a distinct morphometric divergence in the South China Sea population along PC1, which accounted for 45.15% of the observed variation. This strong differentiation suggests potential stock separation, likely driven by genetic isolation, localized environmental factors, or region-specific evolutionary pressures. These findings align with previous studies that emphasized the role of oceanographic barriers and habitat heterogeneity in shaping population structure within marine species [38-40]. Conversely, the overlapping distributions of individuals from the Malacca Strait, Sulu Sea, and Celebes Sea indicate high morphometric similarity, which may be indicative of significant population connectivity. This pattern suggests possible gene flow facilitated by ocean currents, migration pathways, or shared ecological conditions across these regions [38,41]. The observed morphological resemblance supports hypotheses of a broadly connected metapopulation, where intermixing and environmental homogenization play critical roles in maintaining structural uniformity [42,43]. Since there have been no prior reports on the stock structure of P. aneus in Malaysia, it is obvious that it has been managed as a single panmictic population. However, the multivariate analyses in this study reveal otherwise. Aside from the PCA, the CA, UMAP, and DFA results further elucidated two morphological clusters, including the distinct SCS cluster and the SM, SS, and CS cluster (Fig. 4). The findings from the cluster analysis align with those of Kachi et al. [33], who identified two distinct morphological clusters within P. aneus populations from northern Peninsular Malaysia. This pattern is consistent with similar studies on other marine species, where distinct groupings have also been reported. For example, Ramya et al. [44] reported two stock structure clusters for Barbodes carnaticus populations in India, while El Maghazi et al. [45] reported comparable clustering in Trachus trachus populations from Morocco. Likewise, Siddik et al. [21] revealed two morphological groups of Sillaginopsis panijus populations in Bangladesh. This clustering pattern seems to be shaped by the unique geographical and environmental conditions of each region, where ocean currents, depth variations, shifts in salinity, and seasonal monsoons all influence how marine species move and interact [46]. The unique environmental attributes of the South China Sea, as affirmed by Chen et al. [47], include clearer, deeper waters and intricate hydrodynamics, high humidity, and temperature, with minimal climate variation which may have caused unique selection pressures, leading to morphological adaptations including body shape and size variations tailored to these circumstances, as observed in this study. On the other hand, the Strait of Malacca, characterized by shallow depth, high turbidity, and variable salinity due to freshwater intrusion and sedimentation, may have facilitated morphological adaptations for improved manoeuvrability and foraging efficiency [48,49]. The morphological similarities observed among populations in the Strait of Malacca, the Sulu Sea, and the Celebes Sea may be due to shared habitat characteristics, such as soft sediment substrates and similar trophic resources, facilitating gene flow or phenotypic convergence among these regions [38,50]. The DFA biplot illustrates the variation between populations based on morphometrics (Fig. 7), indicating that the South China Sea group is distinct from the others. This is supported by an 88.09% classification accuracy shown in the confusion matrix (supplementary Table S3), suggesting that the P. aneus population from this marine region is a morphologically unique stock. Among the four Malaysian marine regions, DFA achieved an overall correct classification rate of 71.65% for P. aneus populations, reflecting a moderate to high level of morphological differentiation and confirming the reliability of using morphometrics to distinguish regional populations. We believe that phenotypic plasticity in response to local environmental conditions, intermediate morphotypes, and seasonal or developmental changes not included in our current morphometric dataset may explain the 28.35% misclassification rate. The 0% classification accuracy for the Sulu Sea and Celebes Sea naturally groups them with the nearby marine region. This suggests a lack of a distinct morphometric identity, considering the Sulu and Celebes populations as a single stock and those of the Strait of Malacca. Although the small sample size might have contributed to the lack of distinction, the Sulu Sea's extensive coral reefs and complex benthic habitats likely favour morphologies adapted for reef-associated niches [51]. Conversely, the Celebes Sea's deep, nutrient-rich upwelling zones promote high productivity, likely due to more efficient feeding and growth [52]. The classification accuracy in this study is comparable to the 79% reported for the sciaenid Isopisthus parvipinnis (bigtooth corvina) from the southwest Atlantic Ocean, using geometric morphometrics, and the 88.6% accuracy observed in populations of the tuna Euthynnus affinis in Malaysia. However, the accuracy in this study differs from the 67.3% reported for the geometric morphometrics of P. aneus populations in northern Peninsular Malaysia [33]. It also varies from the 68.39% reported for species like Amblygaster clupeoides in Bangladesh [36], probably due to overlapping morphometric features among the populations studied. In this study, ED, SnL, CPD, and TL were among the predictor variables used to distinguish P. aneus populations, with eye diameter being the most significant. This finding has ecological importance for understanding how P. aneus adapts to different marine environments. For example, the South China Sea features diverse habitats, including turbid estuaries and clearer offshore waters [53]. Variations in ED and SnL among P. aneus populations could reflect adaptations to light conditions and prey availability, such as larger eye diameters enhancing vision in deeper waters and longer snouts aiding in probing substrates for benthic prey. Additionally, changes in ED and SnL may indicate adaptations to specific lighting and prey types found in coral reef habitats like the Sulu Sea [54]. Conversely, a higher CPD could improve manoeuvrability and swimming stability, especially in shallow, narrow channels with strong tidal currents and fluctuating salinity, such as the Strait of Malacca [55]. Compared to earlier reports, the morphological discrimination of the same species in northern Peninsular Malaysia involved different morphometric traits, including standard length, body depth, total length, snout length, and eye diameter, ranked in order of increasing importance [33]. Likewise, Utarini et al. [56] found that differences in head and caudal regions helped distinguish between male and female P. aneus in central Java, Indonesia. The study of morphometrics in fish provides crucial insights into phenotypic population units [7,17,36,44,57,58] as confirmed by our findings. The evidence from morphometric analyses in this study suggests that the separation of the South China Sea P. aneus population from the other population favours it as a distinct, homogenous stock morphometrically, owing to the region’s ecological uniqueness. In contrast, the Malacca Strait-Celebes-Sulu Sea populations formed a second, more heterogeneous stock, with overlapping morphologies. This result does not completely agree with the assumption that the P. aneus population in Malaysia is a single stock. To support this observed morphological divergence, the molecular work of Lo et al. [59] on Pennah croakers in Southeast Asia revealed two mitochondrial lineages of P. aneus , with the confinement of one to the South China Sea. Our findings suggest that two stocks of P. aneus exist in Malaysian waters: the South China Sea stock, which should be managed as a separate unit, and the Strait of Malacca-Celebes-Sulu Sea stock, which may require management approaches that are integrated. However, the results obtained in this study are not conclusive, but would require validation from the molecular analysis of our data. Conclusion There is no doubt that natural resource management, biodiversity protection, and fisheries management rely on species identification and population discrimination. Our study revealed two morphological stocks of P. aneus in Malaysia, which we believe could be managed separately and highlights the utility of multivariate morphometric analysis as an informative and cost-effective technique for preliminary stock discrimination, especially for fisheries species with poor stock structure data documentation. Nevertheless, the morphometric data needs to be further complemented with molecular investigation to determine the extent of gene flow and reproductive isolation. This will provide a strong basis for understanding the stock structure information about P. aneus populations across Malaysian waters. This awareness will aid in the safeguarding and effective management of P. aneus populations in Peninsular Malaysia and Borneo. Materials and Methods Ethical Statement No live fish samples were used in this study as specimens were collected dead from the local fishermen; hence, ethical review and approval were not required. Also, P. aneus species is not endangered or considered protected, and all sampling complied with the institutional and legal requirements for animal use in scientific research. Sample collection A total of 423 samples of P. aneus from Peninsular Malaysia and Borneo were collected between June 2023 and October 2024. Twenty-two fish landing sites and their coordinates were identified for P. aneus collection, encompassing the major seas around Malaysia including the Strait of Malacca (SM), the South China Sea (SCS), the Sulu Sea (SS), and the Celebes Sea (CS) (Table 1, Fig. 1). Along the Strait of Malacca, 11 fish landing sites including Kuala Kedah, Kota Kuala Muda, Kuala Muda Penaga, Batu Maung, Kuala Kurau, Pantai Remis, Kuala Selangor, Pantai Teluk Kemang, Pasir Panjang, Kuala Sungai Baru, and Tanjong Kling comprised the first population of P. aneus . Within the South China Sea, seven sampling sites: Sematan, Kuching, Mukah, Bintulu, and Miri in Sarawak, Labuan, and Kota Kinabalu in Sabah, constituted the second P. aneus population. Two sampling sites, Kudat and Sandakan in Sabah, represented the third P. aneus population from the Sulu Sea. Finally, the Celebes Sea was represented by two sampling sites: Lahad Datu and Tawau in Sabah, comprising the fourth P. aneus population. All the samples were packaged and transported to the Molecular Ecology Laboratory at the School of Biological Sciences, Universiti Sains Malaysia. Identification and validation of all samples were achieved using Southeast Asia’s field guide for marine fishes and crustaceans [60]. A digital camera (Olympus TG-5, Japan) was used to take photographs of neatly rinsed fish samples on a black background to enhance visibility. The morphometric variables (Fig. 2) were measured via a digital calliper with a precision of 0.1 cm and descriptively summarized using SPSS (ver. 29) (Supplementary Table S2). For long-term storage, voucher specimens were fixed in formalin (4%) and stored in ethanol (70%) at the Molecular Ecology Laboratory, School of Biological Sciences, Universiti Sains Malaysia. Standardization of morphometric variables The Elliott et al. [61] equation was utilized in this study to mitigate the impact of size-related effects, thereby minimizing fluctuations and facilitating more precise comparisons of other relevant variables. The expression b was used to transform the morphometrics, where M equals the original measurement, Madj equals the size-adjusted measurement, Ls equals the overall mean of the standard length for all samples, L o equals each sample’s standard length, and b represents the slope of the log M/log L o regression encompassing the fish populations. The adjusted variables were correlated with the SL to ensure that the effect of size had been removed with non-significant p values (p >0.05) (Supplementary Table S3) [61]. Multivariate morphometric analyses All the size-adjusted morphometric variables, excluding the standard length, were used for the multivariate analyses. This was done to avoid reintroducing the size-related effect that had been corrected. Principal Component Analysis was applied to the standardized data to identify the main axes of variation and to reduce dimensionality. PCA used the high-dimensional morphometric data and projected it onto orthogonal principal components (PCs) that explain the most variation sequentially. We computed PCA using scikit-learn and examined the variance explained by each component. Scores for the first two principal components (PC1 and PC2) were plotted for all individuals, with points coloured by marine region. This allowed for the assessment of the specimens clustering in the reduced morphospace. The morphological relationships among the four P. aneus populations were examined using hierarchical cluster analysis (CA). The mean vector of each region was computed, and the pairwise Mahalanobis distances between region centroids were calculated. SciPy’s average-linkage algorithm used these distances to construct a dendrogram, illustrating the relative dissimilarity of regions based on their morphometric profiles. The uniform manifold approximation and projection (UMAP), a non-linear dimensionality reduction algorithm, was used to examine nonlinear patterns and potential grouping in the dataset. The umap-learn library was used to perform UMAP, projecting high-dimensional morphometric data for visualization into two dimensions. Matplotlib and seaborn were used to plot UMAP coordinates, examining regional clustering and possible stock structure [62]. UMAP is particularly effective for morphometric studies of fish populations, as it can reveal hidden biological variations within complex datasets [63]. This study used random forest (RF) analysis to identify the most important morphometric features for population differentiation, focusing on feature significance scores. This method ensures accurate variable ranking, highlights only relevant attributes, and improves the interpretability and reliability of the results [64]. Scikit-learn’s Random Forest classifier was employed to analyze regional differences using morphometric variables. The model, trained with 100 trees (default parameters), and its out-of-bag accuracy were recorded to enhance classification robustness and accuracy. The feature importance was determined using the mean reduction in Gini impurity and permutation importance to identify the morphometric variables that effectively differentiate the populations. Discriminant function analysis (DFA) was used to evaluate the separation of designated groups, identifying linear combinations of features that maximize between-group variation. The model was trained using region labels, converted into discriminant axes, and illustrated with region centroids and 95% confidence ellipses. DFA was utilized to classify each fish sample into predetermined groups, including the prediction of the classification accuracy of the different populations. All statistical analyses, including data processing, dimensionality reduction, and visualization, were performed in Python (ver. 3.11.12) software (https://www.python.org/downloads/release/python-3112/) for the Google Colab environment, using standard scientific libraries such as Pandas, Numpy, SciPy, and scikit-learn [65] . Declarations Data availability All data used in this study are included in this article, and the additional supplementary data are also available. Raw data can be obtained by request from the corresponding author. Declaration of competing interests The authors declare no competing interests. Acknowledgements The authors would like to thank the School of Biological Sciences and Universiti Sains Malaysia for the opportunity and enabling environment to perform this research. Authors contribution J.B.K. and D.M.N. designed the study; J.B.K. and N.W.B.H. collected the fish samples and conducted morphometric measurements; J.B.K. prepared the first draft of the manuscript; J.B.K. and K.B. conducted the statistical analysis; J.B.K. and D.M.N. critically revised the manuscript. All authors read and approved the final manuscript. Funding This present study was not funded by any grant. The author J.B.K was only sponsored for his doctoral studies at the Universiti Sains Malaysia by the Tertiary Education Trust Fund (TETFUND) in Nigeria, under the approval of the Federal University Lokoja, Nigeria. References Myers, N., Mittermeler, R. A., Mittermeler, C. G., Da Fonseca, G. A. B & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403 , 853–858. https://doi.org/10.1038/35002501 (2000). Asaad, I., Lundquist, C. J., Erdmann, M. V. & Costello, M. J. Delineating priority areas for marine biodiversity conservation in the Coral Triangle. Biol. Conservation 222 , 198-211. https://doi.org/10.1016/j.biocon.2018.03.037 (2018). Tong, P. S. More policies and laws, is it better for biodiversity conservation in Malaysia? Conservation Sci. Practice 2 (8), 1–11. https://doi.org/10.1111/csp2.235 (2020). Latiff, A. Biodiversity in Malaysia. In Global biodiversity (pp. 307-349). Apple Academic Press. (2018). Al-Qadami, E., Razi, M. A. M., Ideris, M. F. M., Mahamud, M., Shah, S. M. H., Pu, J. H., Amran, M., Avudaiappan, S. & Figueroa, A. G. Comprehensive biodiversity assessment of flora and fauna along the Kedah coastline: Implications for conservation and sustainable management. Results in Engineering 24 , 103120. https://doi.org/10.1016/j.rineng.2024.103120 (2024). Imtiaz, A., Duong, T. Y., Nor, S. A. M. & Naim, D. M. Molecular identification of commercially important species of Nemipterus (Perciformes: Nemipteridae) in surrounding seas of Malaysia. Biodiversitas, 17 (2), 571–577. https://doi.org/10.13057/biodiv/d170226 (2016). Binashikhbubkr, K., Kachi, J. B., Al-misned, F. & Naim, D. 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/https://doi.org/10.1016/j.jksus.2024.103278 (2024). Faizal, E. M., Jamon, S., Jamaludin, N. A. & Halim, N. H. A. Status of neritic tuna fishery and some biological aspects of Kawakawa ( Euthynnus affinis ) in the northern part of Peninsular Malaysia. IOTC–2019–WPNT09 – 13 , 1–5 (2019). Bakar, A. A., Adamson, E. A. S., Juliana, L. H., Nor Mohd, S. A., Wei-Jen, C., Man, A. & Naim Md, D. DNA barcoding of Malaysian commercial snapper reveals an unrecognized species of the yellow-lined Lutjanus (Pisces: Lutjanidae). PLoS ONE, 13 (9), 1–12. https://doi.org/10.1371/journal.pone.0202945 (2018). (2018) Harun, M., Sulaiman, N. & Ismail, R. Journal of Advanced Zoology Status of Fish in Food Security in Malaysia Article. J Adv Zool , 44 (3), 138–155. https://doi.org/https://ja zindia.com. (2023). Department of Fisheries, Malaysia. Annual Fisheries Statistics. https://www.dof.gov.my/en/resources/i-extension-en/annual-report/ (2023) Accessed May 20, 2025. Froese, R. & Pauly, D. (Eds.). FishBase. World Wide Web electronic publication, www.fishbase.org. (2024) (Accessed 7 th December, 2024). Lim, H. C., Habib, A. & Chen, W. J. Comparative Phylogeography and Phylogeny of Pennah Croakers (Teleostei: Sciaenidae) in Southeast Asian Waters. Genes, 12 (12), 1–21. https://doi.org/10.3390/genes12121926 (2021). Wagiyo, K., Tirtadanu, T. & Chodriyah, U. Biology characteristic, abundance index and fishing aspect of donkey croaker ( Pennahia anea Bloch, 1793) in the Tangerang Waters. E3S Web of Conferences , 153 (1), 1–11. https://doi.org/https://doi.org/10.1051/e3sconf /2020153010 11. (2020). Chao, L., Santos, M., Seah, Y. G., Wong, L., Kar-Hoe, K. H., Hadiaty, R. K., Suharti, S., Russell, B., Larson, H. & Shah, N. H. A. The IUCN Red List of Threatened Species 2020: e.T201075A2690611, pp1–10. https://doi.org/10.2305/IUCN.UK.2020-1.RLTS.T49188506A49236557.en (2020). Third, G. M. & Parsons, D. M. Population identification of snapper ( Chrysophrys auratus ) using body Geometric Morphometrics to inform sustainable fisheries management. Fish Res , 280 , https://doi.org/10.1016/j.fishres.2024.107159 (2024). Cadrin, S. X. Advances in morphometric identification of fishery stocks. Rev. Fish Biol. Fisheries , 10 (1), 91–112. http://dx.doi.org/10.1023/A:1008939104413 (2000). Meshram, M., Animal, K. V., Rajesh, M. & Suyani, N. K. Morphological measurements, length weight relationship and relative condition factor (Kn) of Obtuse barracuda Sphyraena obtusata (Cuvier, 1829) from Southeastern Arabian Sea. Indian J. Geo. Mar. Sci . 50 (6), 480–488. (2021). Rasheeq, A. A., Rajesh, M., Kumar, T. T. A., Rajesh, K. M., Kathirvelpandian, A., Kumar, S. & Singh, P. K. Stock structure analysis of the white-spotted spine foot fish ( Siganus canaliculatus ) along the Indian coast using Truss morphometry. Reg. Stud. Mar. Sci . 65 , 103072. https://doi.org/10.1016/j.rsma.2023.103072 (2023). Mahfuj, S., Islam, S. I., Jinia, S. S., Hossain, M. F. & Atique, U. Stock identification of Congaturi halfbeak ( Hyporhamphus limbatus ): insight into conventional and truss-based morphometrics. J. Basic Appl. Zool . 84 , 10. https://doi.org/10.1186/s41936-023-00329-7 (2023). Siddik, M. A. B., Hanif, M. A., Chaklader, M. R., Nahar, A. & Mahmud, S. Fishery biology of gangetic whiting Sillaginopsis panijus (Hamilton, 1822) endemic to Ganges delta, Bangladesh. Egyptian J. Aquat. Res. 41 (4), 307–313. https://doi.org/10.1016/j.ejar.2015.11.001 (2016). Cheng, Q., Lu, D. & Ma, L. Morphological differences between close populations discernible by multivariate analysis: A case study of genus Coilia (Teleostei: Clupeiformes). Aqua. Liv. Res . 18 , 187–192. https://doi.org/10.1051/alr:2005020 (2005). Moreira, C., Froufe, E., Vaz-pires, P., Triay, R. & Correia, A. T. Landmark-based geometric morphometrics analysis of body shape variation among populations of the blue jack mackerel, Trachurus picturatus , from the North-East Atlantic. J. Sea Res . 163 , 101926. https://doi.org/10.1016/j.seares.2020.101926 (2020). Hajisamae, S., Iqbal, T. H., Soe, K. K. & Wang, W. X. Phenotypic variation of protandrous hermaphrodite fish Eleutheronema tetradactylum (Shaw, 1804) from Thailand coastal waters. J. Sea Res. 196 , https://doi.org/10.1016/j.seares.2023.102448 (2023). Iqbal, M. Z., Chamily, F. A., Rahman, M. M., Tasnim, R., Mohiuddin, M., Sultana, F., Rahman, S. M., Abdullah, A. M., Ali, M. M. & Asaduzzaman, M. Habitat salinity and source-induced variation in body shape of euryhaline long whiskers catfish ( Mystus gulio ). Reg. Stud. Mar. Sci. 69 , 103308. https://doi.org/10.1016/j.rsma.2023.103308 (2024). Kaouèche, M., Bahri-Sfar, L., Hammami, I. & Hassine, O. K. B. Morphometric variations in white seabream Diplodus sargus (Linneus, 1758) populations along the Tunisian coast. Oceanologia, 59 (2), 129–138. https://doi.org/10.1016/j.oceano.2016.10.003 (2017). Jalili, P., Eagderi, S. & Keivany, Y. Body shape comparison of kura bleak ( Alburnus filippii ) in Aras and Ahar-Chai Rivers Using geometric morphometric approach. Res. Zool. 5 , 20-24. https://doi.org/10.5923/j.zoology.20150501.03 (2015). Aminan, A. W., Lim, W. K. L., Chung, H. H. & Sulaiman, B. Morphometric analysis and genetic relationship of Rasbora spp. In Sarawak, Malaysia. Trop. Life Sci. Res. 31 (2), 33–49. https://doi.org/10.21315/tlsr2020.31.2.3 (2020). Binashikhbubkr, K., Malik, A., Al-Misned, F., Mahboob, S. & Md Naim, D. Geometric morphometric discrimination between seven populations of Kawakawa Euthynnus affinis (Cantor, 1849) from Peninsular Malaysia. J. King Saud Uni-Sci. 34 (3), 101863. https://doi.org/10.1016/j.jksus.2022.101863 (2022). Fadli, N., Damora, A., Abidin, Z., Ramadhaniaty, M., Muhammad, N., Riska, S., Macusi, E. D., Habib, A. & Siti-azizah, M. N. Morphometric and genetic variations of three grouper species (genus Epinephelus ) from the northern region of Aceh province, Indonesia. Zoologischer Anzeiger 307 , 89–95. https://doi.org/10.1016/j.jcz.2023.10.002 (2023). Shukri, S. M., Binashikhbubkr, K., Setyawan, A. D. & Naim, D. M. Geometric morphometric divergence of five populations of Pampus argenteus (Euphrasen, 1788) from Malaysian waters. Nusant. Biosci . 16 (1), 1-12. https://doi.org/10.13057/nusbiosci/n160101 (2024). Hamid, M. A., Sah, A. S. R. M., Idris, I., Nor, S. A. M., Ghaffar, M. A., Peng, C. N. S. & Husin, S. M. The Diversity and Distribution of Fish at the Port and Urbanized Areas of Pulau Pinang Strait, Malaysia. Sains Malaysiana 52 (4), 1047–1058. https://doi.org/http://doi.org/10.17576/jsm-2023-5204-02 (2023). Kachi, J. B., Binashikhbubkr, K. & Naim, D. M. Morphological differentiation of Pennahia aneus (Bloch, 1793 ) populations from Northern Peninsular Malaysia using geometric morphometrics. Nusant. Biosc. 17 (1), 30–38. https://doi.org/10.13057/nusbiosci/n170104 (2025). Hoff, N. T., Dias, J. F., de Lourdes Zani-Teixeira, M., Soeth, M. & Correia, A. T. Population structure of the bigtooth corvina Isopisthus parvipinnis from the Southwest Atlantic Ocean as determined by whole-body morphology. Reg. Stud. Mar. Sci. 39 , 101379. https://doi.org/10.1016/j.rsma.2020.101379 (2020). Asuquo, P. E. & Ifon, T. H. Morphometric adaptation of Bobo Croaker Morphometric adaptation of Bobo Croaker, Pseudotolithus elongatus (Bowdich, 1825) (Sciaenidae) in the Cross-River estuary, Nigeria. Internat. J. Natur. Appl. Sci . 12 , 87–92. (2019). Hanif, M. A., Siddik, M. A. B., Islam, M. A., Chaklader, M. R. & Nahar, A. Multivariate morphometric variability in sardine, Amblygaster clupeoides (Bleeker, 1849), from the Bay of Bengal coast, Bangladesh. J. Basic Appl. Zool. 80 , 53. https://doi.org/10.1186/s41936-019-0110-6(2019). Imtiaz, A. & Md Naim, D. Geometric morphometrics species discrimination within the genus Nemipterus from Malaysia and its surrounding seas. Biodiversitas 19 (6), 2316–2322. https://doi.org/10.13057/biodiv/d190640 (2018). Pascual, M., Rives, B., Schunter, C. & Macpherson, E. Impact of life history traits on gene flow: A multispecies systematic review across oceanographic barriers in the Mediterranean Sea. PLoS ONE, 12 (5), e0176419. https://doi.org/10.1371/journal.pone.0176419 (2017). Hirschfeld, M., Dudgeon, C., Sheaves, M. & Barnett, A. Barriers in a sea of elasmobranchs: From fishing for populations to testing hypotheses in population genetics. Global Ecol. Biogeo. 30 (11), 2147-2163. https://doi.org/10.1111/geb.13379 (2021). Legrand, T., Fragkopoulou, E., Vapillon, L., Gouvêa, L., Serrão, E. A. & Assis, J. Unravelling the role of oceanographic connectivity in the distribution of genetic diversity of marine forests at the global scale. Global Ecol. Biogeo. 33 (8), e13857. https://doi.org/10.1111/geb.13857 (2024). Krueck, N. C., Treml, E. A., Innes, D. J. & Ovenden, J. R. Ocean currents and the population genetic signature of fish migrations. Ecol , 101 (3), e02967.https://doi.org/10.1002/ecy.2967(2020). Jacob, S., Bestion, E., Legrand, D., Clobert, J. & Cote, J. Habitat matching and spatial heterogeneity of phenotypes: implications for metapopulation and metacommunity functioning. Evol. Ecol. 29 , 851-871. https://doi.org/10.1007/s10682-015-9776-5(2015). Treindl, A. D., Stapley, J. & Leuchtmann, A. Genetic diversity and population structure of Epichloë fungal pathogens of plants in natural ecosystems. Frontiers Ecol Evol. 11 , 1129867. https://doi.org/10.3389/fevo.2023.1129867 (2023). Ramya, V. L., Behera, B. K., Das, B. K., Krishna, G., Pavankumar, A. & Pathan, M. K Stock structure analysis of the endemic fish, Barbodes carnaticus (Jerdon 1849), for conservation in a biodiversity hotspot. Environ. Sci. Pol. Res. 28 , 55277-55289. https://doi.org/10.1007/s11356-021-14818-1 (2021). El Mghazli,, H., Znari, M. & Mounir, A. Stock Discrimination in the Horse Mackerel Trachurus trachurus (Teleostei: Carangidae) off the Moroccan Atlantic Coastal Waters using a Morphometric-Meristic Analysis. Thalassas: An Int J Mar Sci. 1-11. https://doi.org/10.1007/s41208-021-00372-7(2021). Zhao, Q., Basher, Z. & Costello, M. J. Mapping near surface global marine ecosystems through cluster analysis of environmental data. Ecol. Res. 35 (2), 327-342. https://doi.org/10.1111/1440-1703.12060 (2020). Chen, X., Jia, H. & Zhang, H. Does the Zhenbei seamount in the South China Sea harbor distinctive biodiversity? A primary study based on eDNA metabarcoding. Global Eco. Cons. 59 , id.e03590. https://doi.org/10.1016/j.gecco.2025.e03590(2025). Alam, L., Zolkaply, S. Z., Sumaila, U. R., Rusydy, I. & Abas, A. Risk of extinction, variability in fish species composition, and factors influencing fish biodiversity in the Malacca Strait. Environ. Sci. Poll Res. 31 (29), 41355–41369. https://doi.org/10.1007/s11356-023-27101-2 (2024). Mustaffa, M., Amirah, E. N., Ahmad, S., Aini, N. A. & Ghazali, R. Tidal of Malacca Straits from AIS Data. J Mar. Res. 21 (I), 120–125. https://doi.org/www.jmr.unican.es (2024). Burress, E. D., Alda, F., Duarte, A., Loureiro, M., Armbruster, J. W. & Chakrabarty, P. Phylogenomics of pike cichlids (Cichlidae: Crenicichla): the rapid ecological speciation of an incipient species flock. J Evol Biol. 31 (1), 14-30. https://doi.org/10.1111/jeb.13196(2018). Green, A., White, A. & Tanzer, J. Integrating fisheries, biodiversity, and climate change objectives into marine protected area network design in the Coral Triangle. Report prepared by The Nature Conservancy for the Coral Triangle Support Partnership, 105 pp. (2012). Sartimbul, A., Winata, V. A., Kasitowati, R. D., Iranawati, F., Rohadi, E., Yona, D., Anjeli, U. G., Pranowo, W. S. & Lauro, F. M. Seasonal Indonesian Throughflow (ITF) across southern Java determines genetic connectivity of Sardinella lemuru (Bleeker, 1835). Deep Sea Research Part II: Top. Stud. Ocean. 209 , 105295. https://doi.org/10.1016/j.dsr2.2023.105295 (2023). Wang, S., Sen, K., He, Y., Bai, M. & Wang, G. Riverine inputs impact the diversity and population structure of heterotrophic fungus-like protists and bacterioplankton in the coastal waters of the South China Sea. Water 14 (10), 1580. https://doi.org/10.3390/w14101580 (2022). Andersson, M. L., Scharnweber, K. & Eklöv, P. Environmental and ecological drivers of eye size variation in a freshwater predator: a trade‐off between foraging and predation risk. Func Ecol 38 (11), 2470-2477. (2024). Mansor, K. N. A. A. K., Roseli, N. H., Ali, F. S. M. & Akhir, M. F. M. Physical properties of seawater in Malacca Strait (Southeast Asia) during monsoon seasons. J Coast. Res , 39 (5), 921-932. https://doi.org/10.2112/JCOASTRES-D-22-00084(2023). Utarini, A. A., Suryaningsih, S. & Nuryanto, A. Truss morphometric and meristic characters of male and female donkey croaker ( Pennahia anea (Bloch, 1793)) taken from Asemdoyong Auction Center Pemalang, Central Java. Jurnal Biota 7 (2), 118-128. https://doi.org/10.19109/Biota.v7i2.8663 (2021). Cadrin, S. X., Karr, L. A. & Mariani, S. Stock Identification Methods: An Overview. In Stock Identification Methods (Issue December:1–5). https://doi.org/10.1016/B978-012154351-8/50002-2 (2005). Kerr, L. A., Hintzen, N. T., Cadrin, S. X., Clausen, L. W., Dickey-Collas, M., Goethel, D. R., Hatfield, E. M. C., Kritzer, J. P. & Nash, R. D. M. Lessons learned from practical approaches to reconcile mismatches between biological population structure and stock units of marine fish. J. Mar. Sci. 74 (6), 1708–1722. https://doi.org/10.1093/icesjms/fsw188 (2017). Lo, P. C., Liu, S. H., Nor, S. A. M., & Chen, W. J. Molecular exploration of hidden diversity in the Indo-West Pacific sciaenid clade. PLoS ONE , 12 (4), e0176623. https://doi.org/10.1371/journal.pone.0176623 (2017). Lim, A. P. K., Ahmad, A., Nor, A. Z. & Mohd, S. N. Field Guide to Fishes and Crustaceans of the Southeast Asian Region. SEAFDEC/MFRMD/39. 246 pp. (2018). Elliott, N. G., Haskard, K. & Koslow, J. A. Morphometric analysis of orange roughy ( Hoplostethus atlanticus ) off the continental slope of southern Australia. J. Fish Biol. 46 , 202–220. https://doi.org/10.1111/j.1095-8649.1995.tb05962.x (1995). McInnes, L., Healy, J. & Melville, J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction . arXiv preprint arXiv:1802.03426 . https://arxiv.org/abs/1802.03426 (2018). Diaz-Papkovich, A., Anderson-Trocme, L. & Gravel, S. UMAP reveals cryptic population structure and phenotype heterogeneity in large genomic cohorts . PLoS Gen, 15 (11), e1008432. https://doi.org/10.1371/journal.pgen.1008432 (2019). Cole, K. E., Yaworsky, P. M. & Hart, I. A. Evaluating statistical models for establishing morphometric taxonomic identifications and a new approach using Random Forest. J. Archaeol. Sci. 143 (4), 105610. http://dx.doi.org/10.1016/j.jas.2022.105610 (2022). Python Software Foundation Python (Version 3.x) [Programming language] . https://www.python.org/ (2023). Additional Declarations No competing interests reported. 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16:24:23","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":34281,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7209099/v1/a4fd48f1133e877093eb8e1c.png"},{"id":96844518,"identity":"523313e0-59f8-4e69-8032-1b159e959f59","added_by":"auto","created_at":"2025-11-26 16:24:23","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9005,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7209099/v1/d65559d57b81dd1536b1610f.png"},{"id":96844523,"identity":"aaf87fc0-52e9-436b-ac4d-7e5e994a6186","added_by":"auto","created_at":"2025-11-26 16:24:23","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":59069,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7209099/v1/8dd738838b3e452d6fbdf108.png"},{"id":96844519,"identity":"152275b6-a332-499a-a750-72bb9be5bf4a","added_by":"auto","created_at":"2025-11-26 16:24:23","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":30159,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7209099/v1/66f97faaff074da5a47c28c4.png"},{"id":96918961,"identity":"627a4b0b-bac2-4e47-9989-d4c1daff7364","added_by":"auto","created_at":"2025-11-27 14:12:53","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":66034,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7209099/v1/8bee9118da321dabbe97148f.png"},{"id":96918441,"identity":"7c4eb77d-478d-48d5-a6b4-624ee303fb16","added_by":"auto","created_at":"2025-11-27 14:11:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":349482,"visible":true,"origin":"","legend":"\u003cp\u003eSampling locations of \u003cem\u003eP. aneus\u003c/em\u003e samples collected from the seas surrounding Peninsular Malaysia and Borneo.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7209099/v1/b85e73aca6481756c2774082.png"},{"id":96844513,"identity":"4585bf16-ca3c-4cb0-a8bb-a7ead20f96fc","added_by":"auto","created_at":"2025-11-26 16:24:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":650255,"visible":true,"origin":"","legend":"\u003cp\u003eMorphometric variables of \u003cem\u003eP. aneus\u003c/em\u003e. ED: eye depth, SnL: snout length, HL: head length, Dpec: distance of pectoral fin, Dpel: distance of pelvic fin, BD: body depth, DA: distance of anal fin, CPD: caudal peduncle depth, SL: standard length, TL: total length.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7209099/v1/0ad76b9835a6c5b2eceeb5e4.png"},{"id":96844508,"identity":"371690db-5c0c-48c3-b84b-d31a05f5c888","added_by":"auto","created_at":"2025-11-26 16:24:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":106213,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Generated principal components (PCs) and variance ratios with their cumulative variances (b)\u003cstrong\u003e \u003c/strong\u003ePrincipal component analysis scatter plot of \u003cem\u003eP. aneus\u003c/em\u003e morphometrics reveals PC1 = 45.14% and PC2 = 20.10% variances.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7209099/v1/d1f2d1ab6245cc0e6296cf36.png"},{"id":96920143,"identity":"ff704760-966f-42e2-b036-3292c255d46d","added_by":"auto","created_at":"2025-11-27 14:14:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":31861,"visible":true,"origin":"","legend":"\u003cp\u003eCluster analysis dendrogram based on the Mahalanobis distances among the four populations of \u003cem\u003eP. aneus\u003c/em\u003e in Peninsular Malaysia and Borneo.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7209099/v1/0401addd21d95b8a1897b1cf.png"},{"id":96918916,"identity":"75b8f84d-6fc9-42dd-ac8b-767b9bc795a5","added_by":"auto","created_at":"2025-11-27 14:12:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":93910,"visible":true,"origin":"","legend":"\u003cp\u003eUniform manifold approximation projection (UMAP) of \u003cem\u003eP. aneus\u003c/em\u003e morphometrics from Peninsular Malaysia and Borneo.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7209099/v1/8a5ad1e46886e91fb02bd66b.png"},{"id":96919244,"identity":"e5b2c23c-b0bd-4034-99d7-126d8c16ad9e","added_by":"auto","created_at":"2025-11-27 14:13:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":33509,"visible":true,"origin":"","legend":"\u003cp\u003eRandom forest ranking of \u003cem\u003eP. aneus\u003c/em\u003e morphometric traits in order of increasing importance.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7209099/v1/da97a16e028b1f052d407095.png"},{"id":96844509,"identity":"09b1165f-58ff-4a1b-8eb4-5353486d102e","added_by":"auto","created_at":"2025-11-26 16:24:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":119467,"visible":true,"origin":"","legend":"\u003cp\u003eDiscriminant function analysis scatter plot of \u003cem\u003eP. aneus\u003c/em\u003emorphometrics showing the different group centroids.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7209099/v1/7806dfaebfe7602871a30c7b.png"},{"id":97135571,"identity":"f24713c2-7cd8-4535-9042-cad2e380c0f1","added_by":"auto","created_at":"2025-12-01 09:51:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2388135,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7209099/v1/f343bc88-5b6a-4d19-854b-953bbb1d2209.pdf"},{"id":96918317,"identity":"b885f32f-2302-4c71-b648-6b6ed47ba1c8","added_by":"auto","created_at":"2025-11-27 14:11:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":192306,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7209099/v1/48e2129922261e54b3c8c992.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Morphometric multivariate analyses reveal population structuring of Pennahia aneus (Perciformes: Sciaenidae) in Malaysian waters","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMalaysia, situated in the Sundaland biodiversity hotspot, is renowned for its significant diversity and endemism [1,2]. It is one of the 17 megadiverse countries worldwide, as identified by Conservation International (CI) for meeting the main criterion of hosting at least 5,000 endemic species [3]. Malaysia\u0026apos;s various ecosystems, including tropical rainforests, mangroves, and coastal habitats, support a wide range of flora and fauna, contributing to its ecological importance at a global scale [4]. Its freshwater and marine ecosystems are crucial for fishery resources. Yet, they are threatened by unsustainable fishing methods, habitat destruction, and climate change, which demand conservation-driven policies and sustainable fisheries management [3,5].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecently, the Malaysian marine fisheries industry has gained significant attention because of its potential to be reckoned with as one of Southeast Asia\u0026rsquo;s prominent fishing hubs [6,7]. This is particularly due to Malaysia\u0026rsquo;s strategic positioning among vital marine ecosystems, which affirms its maritime and rich marine biodiversity status. The important seas bordering Malaysia include the South China Sea, which is situated to the east of Peninsular Malaysia and west of East Malaysia (Sarawak and Sabah); the Strait of Malacca, which is located west of Peninsular Malaysia, between the islands of Sumatra (Indonesia) and Malaysia; the Sulu Sea, which is located northeast of East Malaysia (Sabah); the Celebes Sea, which is situated east of East Malaysia (Sabah), and the Andaman Sea, which is located northwest of Peninsular Malaysia [8]. The Malaysian marine fishing industry plays a crucial role in the national economy by meeting protein needs, creating job opportunities, and facilitating exports to neighbouring countries [9,10]. The annual statistics by the Department of Fisheries, Malaysia, indicated that 1,308,415 metric tonnes of seafood, valued at RM 11.3 billion, were provided in 2022, whereas 1,220,277 metric tonnes, valued at RM 11.44 billion, were contributed in 2023 [11].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePennahia aneus\u003c/em\u003e, is a medium-sized, commercially important demersal fish belonging to the Sciaenidae family, and widely distributed in the Indo-West Pacific region [12], including Malaysia. According to Lim et al. [13] \u003cem\u003eP. aneus\u003c/em\u003e and four other species of the genus \u003cem\u003ePennahia\u003c/em\u003e, collectively known as Pennah croakers, constitute a crucial part of the coastal ichthyofauna essential for subsistence fisheries in Southeast Asian nations. It is suitably marketed fresh, salty, or dried due to its high economic value and delicate flavour [14]. Despite being classified as a species of least concern (LC) by the International Union for Conservation of Nature (IUCN), \u003cem\u003eP. aneus\u003c/em\u003e encounters notable threats like overfishing, especially as bycatch in commercial fisheries, along with the adverse effects of pollution and habitat degradation, which collectively endanger its population and the ecosystems it occupies [15]. According to Third \u0026amp; Parsons [16], overfishing reduces marine biodiversity and threatens ecosystem stability by depleting vulnerable populations and undermining intraspecific diversity, which is essential for resilience against stressors.\u003c/p\u003e\n\u003cp\u003eThe morphometric study of fish is vital for fisheries management, enabling the assessment of evolutionary traits and the detection of changes in morphology, ontogeny, function, and evolutionary connections among individual samples [17,18]. Inadequate consideration of stock structural components in fisheries management plans can lead to population deterioration due to biological changes and decreased productivity rates [19]. Fish species identification relies primarily on external morphological characteristics, including body form, colour patterns, scale size, the number of fins, fin rays, fin spines, and proportional measurements of various body sections, which are used as distinguishing markers to differentiate between species effectively [9,20]. Morphometric studies are cost-effective and straightforward methods for identifying and characterizing fish stocks [21], establishing the layout of fish assortment [22], and distinguishing within or between closely related fish populations [23-25]. Evaluating the morphometric differences of fish is a preliminary method for delineating various stocks [19,26]. Multivariate techniques, including cluster analysis (CA), principal component analysis (PCA), canonical variate analysis (CVA), and discriminant function analysis (DFA), are commonly used to differentiate between populations of the same or different fish species, with numerous studies utilizing these methods to evaluate morphometric variations [7,20,27-31].\u003c/p\u003e\n\u003cp\u003eReports on the stock structure of \u003cem\u003eP. aneus\u003c/em\u003e, using morphometric variations in Malaysian waters, including Borneo, are scarce. Hamid et al. [32] focused only on the distribution and diversity of fish species in the urbanized areas of the Penang Strait, revealing \u003cem\u003eP. aneus\u003c/em\u003e as the third most abundant fish species. However, geometric morphometrics have been used to uncover morphological variations in \u003cem\u003eP. aneus\u003c/em\u003e from northern Peninsular Malaysia [33]. All Sciaenids, including \u003cem\u003eP. aneus\u003c/em\u003e, are generally identified as \u0026lsquo;gelama\u0026rsquo; by the Department of Fisheries, Malaysia, and their total catch at landing sites is rarely differentiated into specific species. This constitutes difficulties for species identification, and the absence of taxonomic knowledge to address this issue could challenge the formulation of effective management strategies for species facing the threat of decline [6]. To address this gap, this study employed multivariate morphometric techniques to delineate four distinct populations of \u003cem\u003eP. aneus\u003c/em\u003e from Peninsular Malaysia and Borneo (Sabah and Sarawak) for the first time, with the expectation of discovering whether unique morphological groups exist that could be considered ideal conservation units for sustainable utilization. The results could also provide essential baseline information on the stock structure of \u003cem\u003eP. aneus\u003c/em\u003e in Malaysian waters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eDescription of sampling locations and size. \u0026nbsp;Note: SM-Straits of Malacca, SCS-South China Sea, SS-Sulu Sea, and CS-Celebes Sea\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSampling Points\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoordinates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarine Region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSize (N)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eKuala Kedah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e6.1072⁰ N; 100.2953⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eKota Kuala Muda, Kedah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e5.5833⁰ N; 100.3833⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eKuala Muda Penaga, Penang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e5.5759⁰ N; 100.3411⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eBatu Maung, Penang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e5.2834⁰ N; 100.2834⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eKuala Kurau, Perak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e5.0150⁰ N; 100.4306⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePantai Remis, Perak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e4.4580⁰ N; 100.6295⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eKuala Selangor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e3.3500⁰ N; 101.2500⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePantai Teluk Kemang, Negeri Sembilan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e2.4538⁰ N; 101.8553⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003ePasir Panjang, Negeri Sembilan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e2.4253⁰ N; 101.9277⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eKuala Sungai Baru, Melaka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e2.3665⁰ N; 102.0122⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eTanjong Kling, Melaka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e2.2173⁰ N; 102.1622⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSematan, Sarawak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1.8064⁰ N; 109.7798⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eKuching, Sarawak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e1.6057⁰ N; 110.4381⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMukah, Sarawak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e2.8974⁰ N; 112.0960⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eBintulu, Sarawak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e3.2408⁰ N; 113.0620⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eMiri, Sarawak\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e4.3990⁰ N; 113.9835⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eLabuan, Sabah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e5.2767⁰ N; 115.2472⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eKota Kinabalu, Sabah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e6.1254⁰ N; 116.1317⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eKudat, Sabah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e6.8805⁰ N; 116.8460⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eSandakan, Sabah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e5.8344⁰ N; 118.0883⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eLahad Datu, Sabah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e5.0183⁰ N; 118.3238⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 170px;\"\u003e\n \u003cp\u003eTawau, Sabah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e4.2425⁰ N; 117.8840⁰ E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e423\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Results","content":"\u003cp\u003ePrincipal component analysis (PCA)\u003c/p\u003e\n\u003cp\u003eThe PCA of the morphometrics\u003cem\u003e\u0026nbsp;\u003c/em\u003eproduced ten principal components (PCs) to analyse the body shape and size differences among the four \u003cem\u003eP. aneus\u003c/em\u003e populations (Fig. 3a). The first principal component (PC1) was the most important with an eigenvalue of 4.53 and a 45.14% variance (Fig.3b). The second principal component (PC2) also had an eigenvalue of 2.01 and a variance of 20.10%. Notably, the major morphometrics accounting for 65.24% of the observed total variations are captured by combining PC1 and PC2 (Fig. 3b), the most important principal components. The scatter plot of PC1 and PC2 (Fig. 3b) shows partial overlap among populations from the Strait of Malacca, Sulu, and Celebes, indicating restricted variation in body size and shape across these marine regions. This suggests a general similarity in morphometric traits among these populations. However, \u003cem\u003eP. aneus\u003c/em\u003e populations from the South China Sea formed a relatively distinct grouping along PC1. This separation highlights a measurable difference in morphometric characteristics from the other populations.\u003c/p\u003e\n\u003cp\u003eCluster analysis (CA)\u003c/p\u003e\n\u003cp\u003eDespite the morphological overlap observed in the PCA, hierarchical cluster analysis (CA) based on Mahalanobis distances identified two distinct morphological clusters among\u0026nbsp;\u003cem\u003eP. aneus\u003c/em\u003e populations (Fig. 4). The first cluster exclusively comprised populations from the South China Sea, reflecting a degree of morphological separation from other regional populations. The second cluster consisted of populations from the Straits of Malacca, with overlapping populations from the Sulu and Celebes Seas. This grouping indicates a closer morphological affinity among populations within these regions, consistent with the partial overlap observed in PCA. The clustering pattern suggests underlying morphological similarities among these populations, reinforcing the notion of shared morphometric characteristics across connected marine environments.\u003c/p\u003e\n\u003cp\u003eUniform manifold approximation and projection (UMAP)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Further visualization of the UMAP (Fig. 5) revealed two broad morphometric clusters, primarily in the South China Sea and other maritime regions, particularly the Strait of Malacca. The significant separation along UMAP 1 indicates different structuring in morphometrics along the east and west coasts of Peninsular Malaysia, requiring further molecular testing to determine whether the cause is environmental adaptation or potential gene flow. A partial separation along UMAP 1 is revealed of the Celebes and Sulu Sea smaller groups, which overlap with the main groups. Along UMAP 2, there is no clear vertical separation, especially between the South China Sea and the Malacca Strait. However, it likely captures intra-group variations, which is suggestive of population sub-structuring as observed by the smaller grouped clusters near the plot origin with an approximate value of UMAP 2 = 9. The compactness and distinctiveness of this group indicate the existence of a cryptic morphometric population that is not geographically defined.\u003c/p\u003e\n\u003cp\u003eThe RF analyzed morphometric features to distinguish \u003cem\u003eP. aneus\u003c/em\u003e populations. Eye diameter (ED) was the most important trait, followed by snout length (SnL), caudal peduncle length (CPD), and total length (TL). These features are essential for classifying populations and understanding how the species adapts to its environment. However, the distance between the first dorsal fin (DD) and the body depth (BD) had the least influence on defining morphological groups. The RF analysis demonstrated that these features are not all equally important for differentiating groups, emphasizing the power of RF analysis in identifying primary morphological factors.\u003c/p\u003e\n\u003cp\u003eDFA revealed morphometric variations between the groups. Three canonical discriminant functions were identified and used for the discriminant function analysis; however, only the first two discriminant functions effectively combined to explain 99.7% of the total morphometric variation. \u0026nbsp;Only the first discriminant function accounted for the variations with a percentage variance of 96.09%. In contrast, the second discriminant function had a percentage variance of 3.61% (Fig. 7). The DFA scatter plot revealed that the four population group centroids were discriminated into two groups. Specifically, one morphometric group encompassed the South China Sea population, and the other group comprised the Straits of Malacca populations with overlapping populations from the Celebes and Sulu Seas (Fig. 7). Interestingly, the DFA corroborated the PCA, CA, and UMAP analysis by revealing two morphometric groups of \u003cem\u003eP. aneus\u003c/em\u003e in Peninsular Malaysia and Borneo.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAssessment of Classification Accuracy\u003c/p\u003e\n\u003cp\u003eThe Discriminant function analysis (DFA) model, using a 30% holdout set of the data, was used to evaluate the classification accuracy for\u0026nbsp;\u003cem\u003eP. aneus\u003c/em\u003e populations, with the results summarized in the confusion matrix (See supplementary Table S1). The overall classification accuracy was\u0026nbsp;\u003cstrong\u003e71.65%\u003c/strong\u003e, representing the proportion of fish samples correctly assigned to their respective groups. The\u0026nbsp;\u003cstrong\u003eSouth China Sea population showed 88.09% classification accuracy\u003c/strong\u003e, with 11.91% being misclassified as the Malacca Strait group. This finding aligns with the PCA and CA results, which consistently highlighted the uniqueness of this population. Similarly, the\u0026nbsp;\u003cstrong\u003eStrait of Malacca population presented a high classification accuracy of 88.52%\u003c/strong\u003e, reinforcing its relatively distinct morphometric characteristics while maintaining some overlap with adjacent populations. Conversely, the\u0026nbsp;\u003cstrong\u003eCelebes and Sulu Sea populations had a 0% classification accuracy\u003c/strong\u003e, indicating substantial morphological similarity with the Malacca Strait population. The absence of accurately classified samples from the\u0026nbsp;\u003cstrong\u003eSulu and Celebes Sea population\u003c/strong\u003e suggests a significant overlap in body size and shape morphometrics with neighbouring regions, reflecting a gradient of morphological traits rather than clear-cut separation. These DFA results strongly affirm the findings from PCA and CA, collectively demonstrating that\u0026nbsp;\u003cem\u003eP. aneus\u003c/em\u003e populations exhibited some degree of morphological differentiation.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe use of morphometric multivariate analyses to investigate the stock structure of \u003cem\u003eP. aneus\u003c/em\u003e across Malaysian waters in this study was successful, as two morphometric groups were revealed. The findings of this study confirm the reliability of multivariate analytic techniques in identifying fish stocks as previously reported [7,29,33,34].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMorphological variation is often observed within and among populations due to the segregation of a population within its native habitat. The within-group variance in morphometrics explained by PC1 and PC2 (Fig. 3b) reinforces the findings from past studies where the initial principal components captured the most morphometric differences. \u0026nbsp;Specifically, the combined effect of PC1 and PC2 accounted for\u0026nbsp;\u003cstrong\u003e65.25% of the total variance\u003c/strong\u003e in this study, highlighting their significant role in explaining morphological variability across\u0026nbsp;\u003cem\u003eP. aneus\u003c/em\u003e populations. This result is consistent with previous studies examining morphometric differentiation in marine fish species. Asuquo \u0026amp; Ifon [35] reported that\u0026nbsp;\u003cstrong\u003ePC1 and PC2 explained 96.9%\u003c/strong\u003e of the total variance in the morphometrics of\u0026nbsp;\u003cem\u003ePseudotolithus elongatus\u003c/em\u003e from the Nigerian Cross River estuary. Similarly, Hanif et al. [36] reported that\u0026nbsp;\u003cstrong\u003ePC1 and PC2 collectively contributed 70.11%\u003c/strong\u003e of the total variance in their study of morphometric variability in\u0026nbsp;\u003cem\u003eAmblygaster clupeoides\u003c/em\u003e (Sardines) from the Bay of Bengal Coast of Bangladesh. However, few studies have reported slightly different patterns of principal component contributions. For example, Kachi et al. [33] reported that the\u0026nbsp;\u003cstrong\u003efirst four principal components\u003c/strong\u003e together explained\u0026nbsp;\u003cstrong\u003e75.86% of the variance\u003c/strong\u003e in\u0026nbsp;\u003cem\u003eP. aneus\u003c/em\u003e populations from northern Peninsular Malaysia, suggesting a more distributed morphometric influence across multiple components. Similarly, Imtiaz \u0026amp; Md Naim [37] and Binashikhbubkr et al. [7] showed that the\u0026nbsp;\u003cstrong\u003efirst four principal components\u003c/strong\u003e accounted for\u0026nbsp;\u003cstrong\u003e80% of the body shape variation\u003c/strong\u003e in\u0026nbsp;\u003cem\u003eNemipterus\u003c/em\u003e species and\u0026nbsp;\u003cstrong\u003e96.42% of the shape variation\u003c/strong\u003e in\u0026nbsp;\u003cem\u003eEuthynnus affinis\u003c/em\u003e within Malaysian waters. These findings highlight variations in the extent to which individual principal components contribute to morphometric differentiation across different fish species and regions. Despite these differences, the consistently high variance explained by principal components in various studies underscores the effectiveness of PCA in capturing morphological distinctions, reinforcing its robustness as a tool for assessing population structure.\u003c/p\u003e\n\u003cp\u003eThe PCA results revealed a distinct morphometric divergence in the South China Sea population along PC1, which accounted for 45.15% of the observed variation. This strong differentiation suggests potential stock separation, likely driven by genetic isolation, localized environmental factors, or region-specific evolutionary pressures. These findings align with previous studies that emphasized the role of oceanographic barriers and habitat heterogeneity in shaping population structure within marine species [38-40]. Conversely, the overlapping distributions of individuals from the Malacca Strait, Sulu Sea, and Celebes Sea indicate high morphometric similarity, which may be indicative of significant population connectivity. This pattern suggests possible gene flow facilitated by ocean currents, migration pathways, or shared ecological conditions across these regions [38,41]. The observed morphological resemblance supports hypotheses of a broadly connected metapopulation, where intermixing and environmental homogenization play critical roles in maintaining structural uniformity [42,43].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince there have been no prior reports on the stock structure of \u003cem\u003eP. aneus\u003c/em\u003e in Malaysia, it is obvious that it has been managed as a single panmictic population. However, the multivariate analyses in this study reveal otherwise. Aside from the PCA, the CA, UMAP, and DFA results further elucidated two morphological clusters, including the distinct SCS cluster and the SM, SS, and CS cluster (Fig. 4). The findings from the cluster analysis align with those of Kachi et al. [33], who identified two distinct morphological clusters within \u003cem\u003eP. aneus\u003c/em\u003e populations from northern Peninsular Malaysia. This pattern is consistent with similar studies on other marine species, where distinct groupings have also been reported. For example, Ramya et al. [44] reported two stock structure clusters for \u003cem\u003eBarbodes carnaticus\u003c/em\u003e populations in India, while El Maghazi et al. [45] reported comparable clustering in \u003cem\u003eTrachus trachus\u003c/em\u003e populations from Morocco. Likewise, Siddik et al. [21] revealed two morphological groups of \u003cem\u003eSillaginopsis panijus\u003c/em\u003e populations in Bangladesh. This clustering pattern seems to be shaped by the unique geographical and environmental conditions of each region, where ocean currents, depth variations, shifts in salinity, and seasonal monsoons all influence how marine species move and interact [46]. The unique environmental attributes of the South China Sea, as affirmed by Chen et al. [47], include clearer, deeper waters and intricate hydrodynamics, high humidity, and temperature, with minimal climate variation which may have caused unique selection pressures, leading to morphological adaptations including body shape and size variations tailored to these circumstances, as observed in this study. On the other hand, the Strait of Malacca, characterized by shallow depth, high turbidity, and variable salinity due to freshwater intrusion and sedimentation, may have facilitated morphological adaptations for improved manoeuvrability and foraging efficiency [48,49]. The morphological similarities observed among populations in the Strait of Malacca, the Sulu Sea, and the Celebes Sea may be due to shared habitat characteristics, such as soft sediment substrates and similar trophic resources, facilitating gene flow or phenotypic convergence among these regions [38,50].\u003c/p\u003e\n\u003cp\u003eThe DFA biplot illustrates the variation between populations based on morphometrics (Fig. 7), indicating that the South China Sea group is distinct from the others. This is supported by an 88.09% classification accuracy shown in the confusion matrix (supplementary Table S3), suggesting that the \u003cem\u003eP. aneus\u003c/em\u003e population from this marine region is a morphologically unique stock. Among the four Malaysian marine regions, DFA achieved an overall correct classification rate of 71.65% for \u003cem\u003eP. aneus\u003c/em\u003e populations, reflecting a moderate to high level of morphological differentiation and confirming the reliability of using morphometrics to distinguish regional populations. We believe that phenotypic plasticity in response to local environmental conditions, intermediate morphotypes, and seasonal or developmental changes not included in our current morphometric dataset may explain the 28.35% misclassification rate. The 0% classification accuracy for the Sulu Sea and Celebes Sea naturally groups them with the nearby marine region. This suggests a lack of a distinct morphometric identity, considering the Sulu and Celebes populations as a single stock and those of the Strait of Malacca. Although the small sample size might have contributed to the lack of distinction, the Sulu Sea\u0026apos;s extensive coral reefs and complex benthic habitats likely favour morphologies adapted for reef-associated niches [51]. Conversely, the Celebes Sea\u0026apos;s deep, nutrient-rich upwelling zones promote high productivity, likely due to more efficient feeding and growth [52]. The classification accuracy in this study is comparable to the 79% reported for the sciaenid \u003cem\u003eIsopisthus parvipinnis\u003c/em\u003e (bigtooth corvina) from the southwest Atlantic Ocean, using geometric morphometrics, and the 88.6% accuracy observed in populations of the tuna \u003cem\u003eEuthynnus affinis\u003c/em\u003e in Malaysia. However, the accuracy in this study differs from the 67.3% reported for the geometric morphometrics of \u003cem\u003eP. aneus\u003c/em\u003e populations in northern Peninsular Malaysia [33]. It also varies from the 68.39% reported for species like \u003cem\u003eAmblygaster clupeoides\u003c/em\u003e in Bangladesh [36], probably due to overlapping morphometric features among the populations studied.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, ED, SnL, CPD, and TL were among the predictor variables used to distinguish \u003cem\u003eP. aneus\u0026nbsp;\u003c/em\u003epopulations, with eye diameter being the most significant. This finding has ecological importance for understanding how \u003cem\u003eP. aneus\u003c/em\u003e adapts to different marine environments. For example, the South China Sea features diverse habitats, including turbid estuaries and clearer offshore waters [53]. Variations in ED and SnL among \u003cem\u003eP. aneus\u003c/em\u003e populations could reflect adaptations to light conditions and prey availability, such as larger eye diameters enhancing vision in deeper waters and longer snouts aiding in probing substrates for benthic prey. Additionally, changes in ED and SnL may indicate adaptations to specific lighting and prey types found in coral reef habitats like the Sulu Sea [54]. Conversely, a higher CPD could improve manoeuvrability and swimming stability, especially in shallow, narrow channels with strong tidal currents and fluctuating salinity, such as the Strait of Malacca [55]. Compared to earlier reports, the morphological discrimination of the same species in northern Peninsular Malaysia involved different morphometric traits, including standard length, body depth, total length, snout length, and eye diameter, ranked in order of increasing importance [33]. Likewise, Utarini et al. [56] found that differences in head and caudal regions helped distinguish between male and female \u003cem\u003eP. aneus\u003c/em\u003e in central Java, Indonesia.\u003c/p\u003e\n\u003cp\u003eThe study of morphometrics in fish provides crucial insights into phenotypic population units [7,17,36,44,57,58] as confirmed by our findings. The evidence from morphometric analyses in this study suggests that the separation of the South China Sea \u003cem\u003eP. aneus\u003c/em\u003e population from the other population favours it as a distinct, homogenous stock morphometrically, owing to the region\u0026rsquo;s ecological uniqueness. In contrast, the Malacca Strait-Celebes-Sulu Sea populations formed a second, more heterogeneous stock, with overlapping morphologies. This result does not completely agree with the assumption that the \u003cem\u003eP. aneus\u003c/em\u003e population in Malaysia is a single stock. To support this observed morphological divergence, the molecular work of Lo et al. [59] on Pennah croakers in Southeast Asia revealed two mitochondrial lineages of \u003cem\u003eP. aneus\u003c/em\u003e, with the confinement of one to the South China Sea. Our findings suggest that two stocks of \u003cem\u003eP. aneus\u003c/em\u003e exist in Malaysian waters: the South China Sea stock, which should be managed as a separate unit, and the Strait of Malacca-Celebes-Sulu Sea stock, which may require management approaches that are integrated. However, the results obtained in this study are not conclusive, but would require validation from the molecular analysis of our data.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThere is no doubt that natural resource management, biodiversity protection, and fisheries management rely on species identification and population discrimination. Our study revealed two morphological stocks of \u003cem\u003eP. aneus\u003c/em\u003e in Malaysia, which we believe could be managed separately and highlights the utility of multivariate morphometric analysis as an informative and cost-effective technique for preliminary stock discrimination, especially for fisheries species with poor stock structure data documentation. Nevertheless, the morphometric data needs to be further complemented with molecular investigation to determine the extent of gene flow and reproductive isolation. This will provide a strong basis for understanding the stock structure information about \u003cem\u003eP. aneus\u003c/em\u003e populations across Malaysian waters. This awareness will aid in the safeguarding and effective management of \u003cem\u003eP. aneus\u003c/em\u003e populations in Peninsular Malaysia and Borneo.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eEthical Statement\u003c/p\u003e\n\u003cp\u003eNo live fish samples were used in this study as specimens were collected dead from the local fishermen; hence, ethical review and approval were not required. Also, \u003cem\u003eP. aneus\u003c/em\u003e species is not endangered or considered protected, and all sampling complied with the institutional and legal requirements for animal use in scientific research.\u003c/p\u003e\n\u003cp\u003eSample collection\u003c/p\u003e\n\u003cp\u003eA total of 423 samples of \u003cem\u003eP. aneus\u003c/em\u003e from Peninsular Malaysia and Borneo were collected between June 2023 and October 2024. Twenty-two fish landing sites and their coordinates were identified for \u003cem\u003eP. aneus\u003c/em\u003e collection, encompassing the major seas around Malaysia including the Strait of Malacca (SM), the South China Sea (SCS), the Sulu Sea (SS), and the Celebes Sea (CS) (Table 1, Fig. 1). Along the Strait of Malacca, 11 fish landing sites including Kuala Kedah, Kota Kuala Muda, Kuala Muda Penaga, Batu Maung, Kuala Kurau, Pantai Remis, Kuala Selangor, Pantai Teluk Kemang, Pasir Panjang, Kuala Sungai Baru, and Tanjong Kling comprised the first population of \u003cem\u003eP. aneus\u003c/em\u003e. Within the South China Sea, seven sampling sites: Sematan, Kuching, Mukah, Bintulu, and Miri in Sarawak, Labuan, and Kota Kinabalu in Sabah, constituted the second \u003cem\u003eP. aneus\u003c/em\u003e population. Two sampling sites, Kudat and Sandakan in Sabah, represented the third \u003cem\u003eP. aneus\u003c/em\u003e population from the Sulu Sea. Finally, the Celebes Sea was represented by two sampling sites: Lahad Datu and Tawau in Sabah, comprising the fourth \u003cem\u003eP. aneus\u003c/em\u003e population. All the samples were packaged and transported to the Molecular Ecology Laboratory at the School of Biological Sciences, Universiti Sains Malaysia. Identification and validation of all samples were achieved using Southeast Asia\u0026rsquo;s field guide for marine fishes and crustaceans [60]. A digital camera (Olympus TG-5, Japan) was used to take photographs of neatly rinsed fish samples on a black background to enhance visibility. The morphometric variables (Fig. 2) were measured via a digital calliper with a precision of 0.1 cm and descriptively summarized using SPSS (ver. 29) (Supplementary Table S2). For long-term storage, voucher specimens were fixed in formalin (4%) and stored in ethanol (70%) at the Molecular Ecology Laboratory, School of Biological Sciences, Universiti Sains Malaysia.\u003c/p\u003e\n\u003cp\u003eStandardization of morphometric variables\u003c/p\u003e\n\u003cp\u003eThe Elliott et al. [61] equation was utilized in this study to mitigate the impact of size-related effects, thereby minimizing fluctuations and facilitating more precise comparisons of other relevant variables. The expression\u0026nbsp;\u0026nbsp;\u003csup\u003eb\u003c/sup\u003e was used to transform the morphometrics, where M equals the original measurement, Madj equals the size-adjusted measurement, Ls equals the overall mean of the standard length for all samples, L\u003csub\u003eo\u003c/sub\u003e equals each sample\u0026rsquo;s standard length, and b represents the slope of the log M/log L\u003csub\u003eo\u003c/sub\u003e regression encompassing the fish populations. The adjusted variables were correlated with the SL to ensure that the effect of size had been removed with non-significant p values (p \u0026gt;0.05) (Supplementary Table S3) [61].\u003c/p\u003e\n\u003cp\u003eMultivariate morphometric analyses\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll the size-adjusted morphometric variables, excluding the standard length, were used for the multivariate analyses. This was done to avoid reintroducing the size-related effect that had been corrected.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrincipal Component Analysis was applied to the standardized data to identify the main axes of variation and to reduce dimensionality. PCA used the high-dimensional morphometric data and projected it onto orthogonal principal components (PCs) that explain the most variation sequentially. We computed PCA using scikit-learn and examined the variance explained by each component. Scores for the first two principal components (PC1 and PC2) were plotted for all individuals, with points coloured by marine region. This allowed for the assessment of the specimens clustering in the reduced morphospace.\u003c/p\u003e\n\u003cp\u003eThe morphological relationships among the four \u003cem\u003eP. aneus\u003c/em\u003e populations were examined using hierarchical\u0026nbsp;cluster analysis (CA). The mean vector of each region was computed, and the pairwise Mahalanobis distances between region centroids were calculated. SciPy\u0026rsquo;s average-linkage algorithm used these distances to construct a dendrogram, illustrating the relative dissimilarity of regions based on their morphometric profiles.\u003c/p\u003e\n\u003cp\u003eThe uniform manifold approximation and projection (UMAP), a non-linear dimensionality reduction algorithm, was used to examine nonlinear patterns and potential grouping in the dataset. The umap-learn library was used to perform UMAP, projecting high-dimensional morphometric data for visualization into two dimensions. Matplotlib and seaborn were used to plot UMAP coordinates, examining regional clustering and possible stock structure [62]. UMAP is particularly effective for morphometric studies of fish populations, as it can reveal hidden biological variations within complex datasets [63].\u003c/p\u003e\n\u003cp\u003eThis study used random forest (RF) analysis to identify the most important morphometric features for population differentiation, focusing on feature significance scores. This method ensures accurate variable ranking, highlights only relevant attributes, and improves the interpretability and reliability of the results [64]. Scikit-learn\u0026rsquo;s Random Forest classifier was employed to analyze regional differences using morphometric variables. The model, trained with 100 trees (default parameters), and its out-of-bag accuracy were recorded to enhance classification robustness and accuracy. The feature importance was determined using the mean reduction in Gini impurity and permutation importance to identify the morphometric variables that effectively differentiate the populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDiscriminant function analysis (DFA) was used to evaluate the separation of designated groups, identifying linear combinations of features that maximize between-group variation. The model was trained using region labels, converted into discriminant axes, and illustrated with region centroids and 95% confidence ellipses. DFA was utilized to classify each fish sample into predetermined groups, including the prediction of the classification accuracy of the different populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; All statistical analyses, including data processing, dimensionality reduction, and visualization, were performed in Python (ver. 3.11.12) software (https://www.python.org/downloads/release/python-3112/) \u0026nbsp;for the Google Colab environment, using standard scientific libraries such as Pandas, Numpy, SciPy, and scikit-learn [65]\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study are included in this article, and the additional supplementary data are also available. Raw data can be obtained by request from the corresponding author.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the School of Biological Sciences and Universiti Sains Malaysia for the opportunity and enabling environment to perform this research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.B.K. and D.M.N. designed the study; J.B.K. and N.W.B.H. collected the fish samples and conducted morphometric measurements; J.B.K. prepared the first draft of the manuscript; J.B.K. and K.B. conducted the statistical analysis; J.B.K. and D.M.N. critically revised the manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis present study was not funded by any grant. The author J.B.K was only sponsored for his doctoral studies at the Universiti Sains Malaysia by the Tertiary Education Trust Fund (TETFUND) in Nigeria, under the approval of the Federal University Lokoja, Nigeria.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMyers, N., Mittermeler, R. A., Mittermeler, C. G., Da Fonseca, G. A. B \u0026amp; Kent, J. Biodiversity hotspots for conservation priorities. \u003cem\u003eNature\u003c/em\u003e\u003cstrong\u003e403\u003c/strong\u003e, 853–858. https://doi.org/10.1038/35002501 (2000). \u003c/li\u003e\n \u003cli\u003eAsaad, I., Lundquist, C. J., Erdmann, M. V. \u0026amp; Costello, M. J. Delineating priority areas for marine biodiversity conservation in the Coral Triangle.\u003cem\u003e Biol. Conservation\u003c/em\u003e\u003cstrong\u003e222\u003c/strong\u003e, 198-211. https://doi.org/10.1016/j.biocon.2018.03.037 (2018).\u003c/li\u003e\n \u003cli\u003eTong, P. S. More policies and laws, is it better for biodiversity conservation in Malaysia? \u003cem\u003eConservation Sci. Practice\u003c/em\u003e\u003cstrong\u003e2\u003c/strong\u003e(8), 1–11. https://doi.org/10.1111/csp2.235 (2020).\u003c/li\u003e\n \u003cli\u003eLatiff, A. Biodiversity in Malaysia. In Global biodiversity (pp. 307-349). Apple Academic Press. (2018). \u003c/li\u003e\n \u003cli\u003eAl-Qadami, E., Razi, M. A. M., Ideris, M. F. M., Mahamud, M., Shah, S. M. H., Pu, J. H., Amran, M., Avudaiappan, S. \u0026amp; Figueroa, A. G. Comprehensive biodiversity assessment of flora and fauna along the Kedah coastline: Implications for conservation and sustainable management. \u003cem\u003eResults in Engineering\u003c/em\u003e\u003cstrong\u003e24\u003c/strong\u003e, 103120. \u003cu\u003ehttps://doi.org/10.1016/j.rineng.2024.103120\u003c/u\u003e (2024). \u003c/li\u003e\n \u003cli\u003eImtiaz, A., Duong, T. Y., Nor, S. A. M. \u0026amp; Naim, D. M. Molecular identification of commercially important species of Nemipterus (Perciformes: Nemipteridae) in surrounding seas of Malaysia. \u003cem\u003eBiodiversitas,\u003c/em\u003e\u003cstrong\u003e17\u003c/strong\u003e(2), 571–577. https://doi.org/10.13057/biodiv/d170226 (2016).\u003c/li\u003e\n \u003cli\u003eBinashikhbubkr, K., Kachi, J. B., Al-misned, F. \u0026amp; Naim, D. Stock structure delineation of Kawakawa \u003cem\u003eEuthynnus affinis\u003c/em\u003e (Cantor, 1849) from Malaysian Borneo using multivariate morphometric analysis. \u003cem\u003eJ. King Saud Uni-Sci. \u003c/em\u003e\u003cstrong\u003e36\u003c/strong\u003e, 1-7. https://doi.org/https://doi.org/10.1016/j.jksus.2024.103278 (2024).\u003c/li\u003e\n \u003cli\u003eFaizal, E. M., Jamon, S., Jamaludin, N. A. \u0026amp; Halim, N. H. A. Status of neritic tuna fishery and some biological aspects of Kawakawa (\u003cem\u003eEuthynnus affinis\u003c/em\u003e) in the northern part of Peninsular Malaysia. \u003cem\u003eIOTC–2019–WPNT09\u003c/em\u003e–\u003cstrong\u003e13\u003c/strong\u003e, 1–5 (2019).\u003c/li\u003e\n \u003cli\u003eBakar, A. A., Adamson, E. A. S., Juliana, L. H., Nor Mohd, S. A., Wei-Jen, C., Man, A. \u0026amp; Naim Md, D. DNA barcoding of Malaysian commercial snapper reveals an unrecognized species of the yellow-lined Lutjanus (Pisces: Lutjanidae). \u003cem\u003ePLoS ONE,\u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e(9), 1–12. https://doi.org/10.1371/journal.pone.0202945 (2018). (2018)\u003c/li\u003e\n \u003cli\u003eHarun, M., Sulaiman, N. \u0026amp; Ismail, R. Journal of Advanced Zoology Status of Fish in Food Security in Malaysia Article. \u003cem\u003eJ Adv Zool\u003c/em\u003e, \u003cstrong\u003e44\u003c/strong\u003e(3), 138–155. https://doi.org/https://ja zindia.com. (2023).\u003c/li\u003e\n \u003cli\u003eDepartment of Fisheries, Malaysia. Annual Fisheries Statistics. https://www.dof.gov.my/en/resources/i-extension-en/annual-report/ (2023) Accessed May 20, 2025.\u003c/li\u003e\n \u003cli\u003eFroese, R. \u0026amp; Pauly, D. (Eds.). FishBase. World Wide Web electronic publication, www.fishbase.org. (2024) (Accessed 7\u003csup\u003eth\u003c/sup\u003e December, 2024).\u003c/li\u003e\n \u003cli\u003eLim, H. C., Habib, A. \u0026amp; Chen, W. J. Comparative Phylogeography and Phylogeny of Pennah Croakers (Teleostei: Sciaenidae) in Southeast Asian Waters. \u003cem\u003eGenes,\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e(12), 1–21. https://doi.org/10.3390/genes12121926 (2021).\u003c/li\u003e\n \u003cli\u003eWagiyo, K., Tirtadanu, T. \u0026amp; Chodriyah, U. Biology characteristic, abundance index and fishing aspect of donkey croaker (\u003cem\u003ePennahia anea\u003c/em\u003e Bloch, 1793) in the Tangerang Waters. \u003cem\u003eE3S Web of Conferences\u003c/em\u003e, \u003cstrong\u003e153\u003c/strong\u003e(1), 1–11. https://doi.org/https://doi.org/10.1051/e3sconf /2020153010 11. (2020).\u003c/li\u003e\n \u003cli\u003eChao, L., Santos, M., Seah, Y. G., Wong, L., Kar-Hoe, K. H., Hadiaty, R. K., Suharti, S., Russell, B., Larson, H. \u0026amp; Shah, N. H. A. The IUCN Red List of Threatened Species 2020: e.T201075A2690611, pp1–10. https://doi.org/10.2305/IUCN.UK.2020-1.RLTS.T49188506A49236557.en (2020).\u003c/li\u003e\n \u003cli\u003eThird, G. M. \u0026amp; Parsons, D. M. Population identification of snapper (\u003cem\u003eChrysophrys auratus\u003c/em\u003e) using body Geometric Morphometrics to inform sustainable fisheries management. \u003cem\u003eFish Res\u003c/em\u003e, \u003cstrong\u003e280\u003c/strong\u003e, https://doi.org/10.1016/j.fishres.2024.107159 (2024).\u003c/li\u003e\n \u003cli\u003eCadrin, S. X. Advances in morphometric identification of fishery stocks. \u003cem\u003eRev. Fish Biol. Fisheries\u003c/em\u003e, \u003cstrong\u003e10\u003c/strong\u003e(1), 91–112. http://dx.doi.org/10.1023/A:1008939104413 (2000).\u003c/li\u003e\n \u003cli\u003eMeshram, M., Animal, K. V., Rajesh, M. \u0026amp; Suyani, N. K. Morphological measurements, length weight relationship and relative condition factor (Kn) of Obtuse barracuda \u003cem\u003eSphyraena obtusata\u003c/em\u003e (Cuvier, 1829) from Southeastern Arabian Sea. \u003cem\u003eIndian J. Geo. Mar. Sci\u003c/em\u003e. \u003cstrong\u003e50\u003c/strong\u003e(6), 480–488. (2021).\u003c/li\u003e\n \u003cli\u003eRasheeq, A. A., Rajesh, M., Kumar, T. T. A., Rajesh, K. M., Kathirvelpandian, A., Kumar, S. \u0026amp; Singh, P. K. Stock structure analysis of the white-spotted spine foot fish (\u003cem\u003eSiganus canaliculatus\u003c/em\u003e) along the Indian coast using Truss morphometry. \u003cem\u003eReg. Stud. Mar. Sci\u003c/em\u003e.\u003cstrong\u003e65\u003c/strong\u003e, 103072. https://doi.org/10.1016/j.rsma.2023.103072 (2023).\u003c/li\u003e\n \u003cli\u003eMahfuj, S., Islam, S. I., Jinia, S. S., Hossain, M. F. \u0026amp; Atique, U. Stock identification of Congaturi halfbeak (\u003cem\u003eHyporhamphus limbatus\u003c/em\u003e): insight into conventional and truss-based morphometrics. \u003cem\u003eJ. Basic Appl. Zool\u003c/em\u003e. \u003cstrong\u003e84\u003c/strong\u003e, 10. https://doi.org/10.1186/s41936-023-00329-7 (2023).\u003c/li\u003e\n \u003cli\u003eSiddik, M. A. B., Hanif, M. A., Chaklader, M. R., Nahar, A. \u0026amp; Mahmud, S. Fishery biology of gangetic whiting \u003cem\u003eSillaginopsis panijus\u003c/em\u003e (Hamilton, 1822) endemic to Ganges delta, Bangladesh. \u003cem\u003eEgyptian J. Aquat. Res.\u003c/em\u003e\u003cstrong\u003e41\u003c/strong\u003e(4), 307–313. https://doi.org/10.1016/j.ejar.2015.11.001 (2016).\u003c/li\u003e\n \u003cli\u003eCheng, Q., Lu, D. \u0026amp; Ma, L. Morphological differences between close populations discernible by multivariate analysis: A case study of genus Coilia (Teleostei: Clupeiformes). \u003cem\u003eAqua. Liv. Res\u003c/em\u003e. \u003cstrong\u003e18\u003c/strong\u003e, 187–192. https://doi.org/10.1051/alr:2005020 (2005).\u003c/li\u003e\n \u003cli\u003eMoreira, C., Froufe, E., Vaz-pires, P., Triay, R. \u0026amp; Correia, A. T. Landmark-based geometric morphometrics analysis of body shape variation among populations of the blue jack mackerel, \u003cem\u003eTrachurus picturatus\u003c/em\u003e, from the North-East Atlantic. \u003cem\u003eJ. Sea Res\u003c/em\u003e. \u003cstrong\u003e163\u003c/strong\u003e, 101926. https://doi.org/10.1016/j.seares.2020.101926 (2020).\u003c/li\u003e\n \u003cli\u003eHajisamae, S., Iqbal, T. H., Soe, K. K. \u0026amp; Wang, W. X. Phenotypic variation of protandrous hermaphrodite fish \u003cem\u003eEleutheronema tetradactylum\u003c/em\u003e (Shaw, 1804) from Thailand coastal waters. \u003cem\u003eJ. Sea Res.\u003c/em\u003e\u003cstrong\u003e 196\u003c/strong\u003e, https://doi.org/10.1016/j.seares.2023.102448 (2023).\u003c/li\u003e\n \u003cli\u003eIqbal, M. Z., Chamily, F. A., Rahman, M. M., Tasnim, R., Mohiuddin, M., Sultana, F., Rahman, S. M., Abdullah, A. M., Ali, M. M. \u0026amp; Asaduzzaman, M. Habitat salinity and source-induced variation in body shape of euryhaline long whiskers catfish (\u003cem\u003eMystus gulio\u003c/em\u003e). \u003cem\u003eReg. Stud. Mar. Sci.\u003c/em\u003e\u003cstrong\u003e69\u003c/strong\u003e, 103308. https://doi.org/10.1016/j.rsma.2023.103308 (2024).\u003c/li\u003e\n \u003cli\u003eKaouèche, M., Bahri-Sfar, L., Hammami, I. \u0026amp; Hassine, O. K. B. Morphometric variations in white seabream \u003cem\u003eDiplodus sargus\u003c/em\u003e (Linneus, 1758) populations along the Tunisian coast. \u003cem\u003eOceanologia,\u003c/em\u003e\u003cstrong\u003e59\u003c/strong\u003e(2), 129–138. https://doi.org/10.1016/j.oceano.2016.10.003 (2017).\u003c/li\u003e\n \u003cli\u003eJalili, P., Eagderi, S. \u0026amp; Keivany, Y. Body shape comparison of kura bleak (\u003cem\u003eAlburnus filippii\u003c/em\u003e) in Aras and Ahar-Chai Rivers Using geometric morphometric approach. \u003cem\u003eRes. Zool.\u003c/em\u003e\u003cstrong\u003e5\u003c/strong\u003e, 20-24. https://doi.org/10.5923/j.zoology.20150501.03 (2015).\u003c/li\u003e\n \u003cli\u003eAminan, A. W., Lim, W. K. L., Chung, H. H. \u0026amp; Sulaiman, B. Morphometric analysis and genetic relationship of \u003cem\u003eRasbora\u003c/em\u003e spp. In Sarawak, Malaysia.\u003cem\u003e Trop. Life Sci. Res.\u003c/em\u003e\u003cstrong\u003e31\u003c/strong\u003e(2), 33–49. https://doi.org/10.21315/tlsr2020.31.2.3 (2020).\u003c/li\u003e\n \u003cli\u003eBinashikhbubkr, K., Malik, A., Al-Misned, F., Mahboob, S. \u0026amp; Md Naim, D. Geometric morphometric discrimination between seven populations of Kawakawa \u003cem\u003eEuthynnus affinis\u003c/em\u003e (Cantor, 1849) from Peninsular Malaysia.\u003cem\u003e J. King Saud Uni-Sci. \u003c/em\u003e\u003cstrong\u003e34\u003c/strong\u003e(3), 101863. https://doi.org/10.1016/j.jksus.2022.101863 (2022).\u003c/li\u003e\n \u003cli\u003eFadli, N., Damora, A., Abidin, Z., Ramadhaniaty, M., Muhammad, N., Riska, S., Macusi, E. D., Habib, A. \u0026amp; Siti-azizah, M. N. Morphometric and genetic variations of three grouper species (genus \u003cem\u003eEpinephelus\u003c/em\u003e) from the northern region of Aceh province, Indonesia. \u003cem\u003eZoologischer Anzeiger\u003c/em\u003e\u003cstrong\u003e307\u003c/strong\u003e, 89–95. https://doi.org/10.1016/j.jcz.2023.10.002 (2023).\u003c/li\u003e\n \u003cli\u003eShukri, S. M., Binashikhbubkr, K., Setyawan, A. D. \u0026amp; Naim, D. M. Geometric morphometric divergence of five populations of \u003cem\u003ePampus argenteus \u003c/em\u003e(Euphrasen, 1788) from Malaysian waters. \u003cem\u003eNusant. Biosci\u003c/em\u003e. \u003cstrong\u003e16\u003c/strong\u003e (1), 1-12. https://doi.org/10.13057/nusbiosci/n160101 (2024).\u003c/li\u003e\n \u003cli\u003eHamid, M. A., Sah, A. S. R. M., Idris, I., Nor, S. A. M., Ghaffar, M. A., Peng, C. N. S. \u0026amp; Husin, S. M. The Diversity and Distribution of Fish at the Port and Urbanized Areas of Pulau Pinang Strait, Malaysia.\u003cem\u003e Sains Malaysiana\u003c/em\u003e\u003cstrong\u003e52\u003c/strong\u003e(4), 1047–1058. https://doi.org/http://doi.org/10.17576/jsm-2023-5204-02 (2023).\u003c/li\u003e\n \u003cli\u003eKachi, J. B., Binashikhbubkr, K. \u0026amp; Naim, D. M. Morphological differentiation of \u003cem\u003ePennahia aneus\u003c/em\u003e (Bloch, 1793 ) populations from Northern Peninsular Malaysia using geometric morphometrics. \u003cem\u003eNusant. Biosc.\u003c/em\u003e\u003cstrong\u003e17\u003c/strong\u003e(1), 30–38. https://doi.org/10.13057/nusbiosci/n170104 (2025).\u003c/li\u003e\n \u003cli\u003eHoff, N. T., Dias, J. F., de Lourdes Zani-Teixeira, M., Soeth, M. \u0026amp; Correia, A. T. Population structure of the bigtooth corvina \u003cem\u003eIsopisthus parvipinnis\u003c/em\u003e from the Southwest Atlantic Ocean as determined by whole-body morphology. \u003cem\u003eReg. Stud. Mar. Sci.\u003c/em\u003e\u003cstrong\u003e39\u003c/strong\u003e, 101379. https://doi.org/10.1016/j.rsma.2020.101379 (2020). \u003c/li\u003e\n \u003cli\u003eAsuquo, P. E. \u0026amp; Ifon, T. H. Morphometric adaptation of Bobo Croaker Morphometric adaptation of Bobo Croaker, \u003cem\u003ePseudotolithus elongatus\u003c/em\u003e (Bowdich, 1825) (Sciaenidae) in the Cross-River estuary, Nigeria. \u003cem\u003eInternat. J. Natur. Appl. Sci\u003c/em\u003e. \u003cstrong\u003e12\u003c/strong\u003e, 87–92. (2019). \u003c/li\u003e\n \u003cli\u003eHanif, M. A., Siddik, M. A. B., Islam, M. A., Chaklader, M. R. \u0026amp; Nahar, A. Multivariate morphometric variability in sardine, \u003cem\u003eAmblygaster clupeoides\u003c/em\u003e (Bleeker, 1849), from the Bay of Bengal coast, Bangladesh. \u003cem\u003eJ. Basic Appl. Zool. \u003c/em\u003e\u003cstrong\u003e80\u003c/strong\u003e, 53. https://doi.org/10.1186/s41936-019-0110-6(2019).\u003c/li\u003e\n \u003cli\u003eImtiaz, A. \u0026amp; Md Naim, D. Geometric morphometrics species discrimination within the genus \u003cem\u003eNemipterus\u003c/em\u003e from Malaysia and its surrounding seas. \u003cem\u003eBiodiversitas\u003c/em\u003e\u003cstrong\u003e19\u003c/strong\u003e(6), 2316–2322. https://doi.org/10.13057/biodiv/d190640 (2018).\u003c/li\u003e\n \u003cli\u003ePascual, M., Rives, B., Schunter, C. \u0026amp; Macpherson, E. Impact of life history traits on gene flow: A multispecies systematic review across oceanographic barriers in the Mediterranean Sea.\u003cem\u003e PLoS ONE,\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e(5), e0176419. https://doi.org/10.1371/journal.pone.0176419 (2017).\u003c/li\u003e\n \u003cli\u003eHirschfeld, M., Dudgeon, C., Sheaves, M. \u0026amp; Barnett, A. Barriers in a sea of elasmobranchs: From fishing for populations to testing hypotheses in population genetics. \u003cem\u003eGlobal Ecol. Biogeo.\u003c/em\u003e\u003cstrong\u003e30\u003c/strong\u003e(11), 2147-2163. https://doi.org/10.1111/geb.13379 (2021).\u003c/li\u003e\n \u003cli\u003eLegrand, T., Fragkopoulou, E., Vapillon, L., Gouvêa, L., Serrão, E. A. \u0026amp; Assis, J. Unravelling the role of oceanographic connectivity in the distribution of genetic diversity of marine forests at the global scale. \u003cem\u003eGlobal Ecol. Biogeo.\u003c/em\u003e\u003cstrong\u003e33\u003c/strong\u003e(8), e13857. https://doi.org/10.1111/geb.13857 (2024).\u003c/li\u003e\n \u003cli\u003eKrueck, N. C., Treml, E. A., Innes, D. J. \u0026amp; Ovenden, J. R. Ocean currents and the population genetic signature of fish migrations. \u003cem\u003eEcol\u003c/em\u003e, \u003cstrong\u003e101\u003c/strong\u003e(3), e02967.https://doi.org/10.1002/ecy.2967(2020).\u003c/li\u003e\n \u003cli\u003eJacob, S., Bestion, E., Legrand, D., Clobert, J. \u0026amp; Cote, J. Habitat matching and spatial heterogeneity of phenotypes: implications for metapopulation and metacommunity functioning. \u003cem\u003eEvol. Ecol.\u003c/em\u003e\u003cstrong\u003e29\u003c/strong\u003e, 851-871. https://doi.org/10.1007/s10682-015-9776-5(2015).\u003c/li\u003e\n \u003cli\u003eTreindl, A. D., Stapley, J. \u0026amp; Leuchtmann, A. Genetic diversity and population structure of Epichloë fungal pathogens of plants in natural ecosystems. Frontiers \u003cem\u003eEcol Evol.\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, 1129867. https://doi.org/10.3389/fevo.2023.1129867 (2023).\u003c/li\u003e\n \u003cli\u003eRamya, V. L., Behera, B. K., Das, B. K., Krishna, G., Pavankumar, A. \u0026amp; Pathan, M. K Stock structure analysis of the endemic fish, \u003cem\u003eBarbodes carnaticus\u003c/em\u003e (Jerdon 1849), for conservation in a biodiversity hotspot. \u003cem\u003eEnviron. Sci. Pol. Res.\u003c/em\u003e\u003cstrong\u003e28\u003c/strong\u003e, 55277-55289. https://doi.org/10.1007/s11356-021-14818-1 (2021).\u003c/li\u003e\n \u003cli\u003eEl Mghazli,, H., Znari, M. \u0026amp; Mounir, A. Stock Discrimination in the Horse Mackerel Trachurus trachurus (Teleostei: Carangidae) off the Moroccan Atlantic Coastal Waters using a Morphometric-Meristic Analysis. \u003cem\u003eThalassas: An Int J Mar Sci.\u003c/em\u003e 1-11. https://doi.org/10.1007/s41208-021-00372-7(2021).\u003c/li\u003e\n \u003cli\u003eZhao, Q., Basher, Z. \u0026amp; Costello, M. J. Mapping near surface global marine ecosystems through cluster analysis of environmental data. \u003cem\u003eEcol. Res.\u003c/em\u003e\u003cstrong\u003e35\u003c/strong\u003e(2), 327-342. https://doi.org/10.1111/1440-1703.12060 (2020).\u003c/li\u003e\n \u003cli\u003eChen, X., Jia, H. \u0026amp; Zhang, H. Does the Zhenbei seamount in the South China Sea harbor distinctive biodiversity? A primary study based on eDNA metabarcoding. \u003cem\u003eGlobal Eco. Cons.\u003c/em\u003e\u003cstrong\u003e59\u003c/strong\u003e, id.e03590. https://doi.org/10.1016/j.gecco.2025.e03590(2025).\u003c/li\u003e\n \u003cli\u003eAlam, L., Zolkaply, S. Z., Sumaila, U. R., Rusydy, I. \u0026amp; Abas, A. Risk of extinction, variability in fish species composition, and factors influencing fish biodiversity in the Malacca Strait. \u003cem\u003eEnviron. Sci. Poll Res.\u003c/em\u003e\u003cstrong\u003e31\u003c/strong\u003e(29), 41355–41369. https://doi.org/10.1007/s11356-023-27101-2 (2024).\u003c/li\u003e\n \u003cli\u003eMustaffa, M., Amirah, E. N., Ahmad, S., Aini, N. A. \u0026amp; Ghazali, R. Tidal of Malacca Straits from AIS Data.\u003cem\u003e J Mar. Res.\u003c/em\u003e\u003cstrong\u003e21\u003c/strong\u003e(I), 120–125. https://doi.org/www.jmr.unican.es (2024).\u003c/li\u003e\n \u003cli\u003eBurress, E. D., Alda, F., Duarte, A., Loureiro, M., Armbruster, J. W. \u0026amp; Chakrabarty, P. Phylogenomics of pike cichlids (Cichlidae: Crenicichla): the rapid ecological speciation of an incipient species flock.\u003cem\u003e J Evol Biol.\u003c/em\u003e\u003cstrong\u003e31\u003c/strong\u003e(1), 14-30. https://doi.org/10.1111/jeb.13196(2018). \u003c/li\u003e\n \u003cli\u003eGreen, A., White, A. \u0026amp; Tanzer, J. Integrating fisheries, biodiversity, and climate change objectives into marine protected area network design in the Coral Triangle. Report prepared by The Nature Conservancy for the Coral Triangle Support Partnership, 105 pp. (2012).\u003c/li\u003e\n \u003cli\u003eSartimbul, A., Winata, V. A., Kasitowati, R. D., Iranawati, F., Rohadi, E., Yona, D., Anjeli, U. G., Pranowo, W. S. \u0026amp; Lauro, F. M. Seasonal Indonesian Throughflow (ITF) across southern Java determines genetic connectivity of Sardinella lemuru (Bleeker, 1835). Deep Sea Research Part II: \u003cem\u003eTop. Stud. Ocean.\u003c/em\u003e\u003cstrong\u003e209\u003c/strong\u003e, 105295. https://doi.org/10.1016/j.dsr2.2023.105295 (2023).\u003c/li\u003e\n \u003cli\u003eWang, S., Sen, K., He, Y., Bai, M. \u0026amp; Wang, G. Riverine inputs impact the diversity and population structure of heterotrophic fungus-like protists and bacterioplankton in the coastal waters of the South China Sea. \u003cem\u003eWater\u003c/em\u003e\u003cstrong\u003e14\u003c/strong\u003e(10), 1580. https://doi.org/10.3390/w14101580 (2022).\u003c/li\u003e\n \u003cli\u003eAndersson, M. L., Scharnweber, K. \u0026amp; Eklöv, P. Environmental and ecological drivers of eye size variation in a freshwater predator: a trade‐off between foraging and predation risk. \u003cem\u003eFunc Ecol\u003c/em\u003e\u003cstrong\u003e38\u003c/strong\u003e(11), 2470-2477. (2024).\u003c/li\u003e\n \u003cli\u003eMansor, K. N. A. A. K., Roseli, N. H., Ali, F. S. M. \u0026amp; Akhir, M. F. M. Physical properties of seawater in Malacca Strait (Southeast Asia) during monsoon seasons. \u003cem\u003eJ Coast. Res\u003c/em\u003e, \u003cstrong\u003e39\u003c/strong\u003e(5), 921-932. https://doi.org/10.2112/JCOASTRES-D-22-00084(2023).\u003c/li\u003e\n \u003cli\u003eUtarini, A. A., Suryaningsih, S. \u0026amp; Nuryanto, A. Truss morphometric and meristic characters of male and female donkey croaker (\u003cem\u003ePennahia anea \u003c/em\u003e(Bloch, 1793)) taken from Asemdoyong Auction Center Pemalang, Central Java. \u003cem\u003eJurnal Biota\u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e (2), 118-128. https://doi.org/10.19109/Biota.v7i2.8663 (2021).\u003c/li\u003e\n \u003cli\u003eCadrin, S. X., Karr, L. A. \u0026amp; Mariani, S. Stock Identification Methods: An Overview. In \u003cem\u003eStock Identification Methods\u003c/em\u003e (Issue December:1–5). https://doi.org/10.1016/B978-012154351-8/50002-2 (2005).\u003c/li\u003e\n \u003cli\u003eKerr, L. A., Hintzen, N. T., Cadrin, S. X., Clausen, L. W., Dickey-Collas, M., Goethel, D. R., Hatfield, E. M. C., Kritzer, J. P. \u0026amp; Nash, R. D. M. Lessons learned from practical approaches to reconcile mismatches between biological population structure and stock units of marine fish. \u003cem\u003eJ. Mar. Sci.\u003c/em\u003e\u003cstrong\u003e74\u003c/strong\u003e(6), 1708–1722. https://doi.org/10.1093/icesjms/fsw188 (2017).\u003c/li\u003e\n \u003cli\u003eLo, P. C., Liu, S. H., Nor, S. A. M., \u0026amp; Chen, W. J. Molecular exploration of hidden diversity in the Indo-West Pacific sciaenid clade. \u003cem\u003ePLoS ONE\u003c/em\u003e, \u003cstrong\u003e12\u003c/strong\u003e(4), e0176623. https://doi.org/10.1371/journal.pone.0176623 (2017).\u003c/li\u003e\n \u003cli\u003eLim, A. P. K., Ahmad, A., Nor, A. Z. \u0026amp; Mohd, S. N. Field Guide to Fishes and Crustaceans of the Southeast Asian Region. SEAFDEC/MFRMD/39. 246 pp. (2018).\u003c/li\u003e\n \u003cli\u003eElliott, N. G., Haskard, K. \u0026amp; Koslow, J. A. Morphometric analysis of orange roughy (\u003cem\u003eHoplostethus atlanticus\u003c/em\u003e) off the continental slope of southern Australia. \u003cem\u003eJ. Fish Biol.\u003c/em\u003e\u003cstrong\u003e46\u003c/strong\u003e, 202–220. https://doi.org/10.1111/j.1095-8649.1995.tb05962.x (1995).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMcInnes, L., Healy, J. \u0026amp; Melville, J. \u003c/strong\u003e\u003cem\u003eUMAP: Uniform Manifold Approximation and Projection for Dimension Reduction\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003earXiv preprint arXiv:1802.03426\u003c/em\u003e\u003cem\u003e.\u003c/em\u003ehttps://arxiv.org/abs/1802.03426\u003cstrong\u003e(2018).\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDiaz-Papkovich, A., Anderson-Trocme, L. \u0026amp; Gravel, S. \u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e \u003c/strong\u003e\u003cem\u003eUMAP reveals cryptic population structure and phenotype heterogeneity in large genomic cohorts\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003ePLoS Gen, \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(11), e1008432.\u003c/em\u003ehttps://doi.org/10.1371/journal.pgen.1008432\u003cstrong\u003e(2019).\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003eCole, K. E., Yaworsky, P. M. \u0026amp; Hart, I. A. Evaluating statistical models for establishing morphometric taxonomic identifications and a new approach using Random Forest. \u003cem\u003eJ. Archaeol. Sci.\u003c/em\u003e\u003cstrong\u003e143 \u003c/strong\u003e(4), 105610. http://dx.doi.org/10.1016/j.jas.2022.105610 (2022).\u003c/li\u003e\n \u003cli\u003ePython Software Foundation \u003cem\u003ePython (Version 3.x) [Programming language]\u003c/em\u003e. https://www.python.org/ (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"morphometric differentiation, Pennahia aneus, population structure, stock assessment, sustainable fisheries","lastPublishedDoi":"10.21203/rs.3.rs-7209099/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7209099/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Pennahia aneus is a commercially important demersal sciaenid fish found in the Indo-Pacific region, yet its population structure in Malaysia remains insufficiently documented. This study aimed to differentiate four populations of P. aneus in Peninsular Malaysia and Borneo using morphometric multivariate analyses. A total of 423 samples were obtained from 22 landing sites across the South China Sea, Celebes Sea, Sulu Sea, and Strait of Malacca. Eleven morphometric characteristics were measured and used for the analyses. Principal component analysis (PCA) revealed minimal variability in body shape and size due to significant overlap among groups. Hierarchical cluster analysis (CA) identified two distinct morphological groups: the South China Sea population and the Strait of Malacca-Celebes-Sulu Sea population. This classification was reinforced by discriminant function analysis (DFA) and uniform manifold approximation projection (UMAP). The most distinctive morphometric traits for differentiating P. aneus populations were eye depth (ED), snout length (SnL), caudal peduncle depth (CPD), and head length (HL). The classification accuracy for these populations was 71.65%. This study provides the first comprehensive insight into the structured population of P. aneus in Malaysia, offering valuable information for conservation and sustainable resource management. However, molecular validation is necessary to refine the population structure further and strengthen effective management strategies.","manuscriptTitle":"Morphometric multivariate analyses reveal population structuring of Pennahia aneus (Perciformes: Sciaenidae) in Malaysian waters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 16:24:19","doi":"10.21203/rs.3.rs-7209099/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-06T10:21:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-04T09:36:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-04T06:50:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-30T16:13:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238343687347168213381739769506131270171","date":"2025-11-28T08:36:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171466074990265957877961984372033262531","date":"2025-11-26T07:05:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128697617294659211450118427269127222765","date":"2025-11-26T06:14:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-19T11:12:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-31T08:32:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T10:42:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-28T09:40:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-25T00:05:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2a91eba5-e97a-4a81-8c5e-5bb23ccfd1a2","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58505492,"name":"Biological sciences/Ecology"},{"id":58505493,"name":"Earth and environmental sciences/Ecology"},{"id":58505494,"name":"Earth and environmental sciences/Ocean sciences"},{"id":58505495,"name":"Biological sciences/Zoology"}],"tags":[],"updatedAt":"2026-05-20T08:39:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 16:24:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7209099","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7209099","identity":"rs-7209099","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}