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Article

Dominant Meristic Traits of Fish and Their Association with Habitat Water Quality Parameters: A Case Study

by
Olha Biedunkova
1,*,
Pavlo Kuznietsov
1,
Vasyl Korbutiak
1,
Alina Petruk
1,
Bardukh Gabrielyan
2,
Jaroslav Andreji
3,
Yulia Grokhovska
1 and
Serhii Konontsev
1
1
Institute of Agroecology and Land Management, National University of Water and Environmental Engineering, Soborna Street 11, 33028 Rivne, Ukraine
2
Scientific Center of Zoloology & Hydroecology of the National Academy of Scienses of Armenia, Yerevan 0014, Armenia
3
Department of Poultry Science and Small Animals Husbandry, Slovak University of Agriculture in Nitra, Triedy Andreja Hlinku 2, 94976 Nitra, Slovakia
*
Author to whom correspondence should be addressed.
Fishes 2025, 10(11), 561; https://doi.org/10.3390/fishes10110561
Submission received: 24 September 2025 / Revised: 28 October 2025 / Accepted: 31 October 2025 / Published: 4 November 2025
(This article belongs to the Section Biology and Ecology)
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Abstract

Fish morphological traits are increasingly recognized as sensitive bioindicators of aquatic ecosystem quality. This study investigated the associations between dominant meristic traits, which are a subset of morphological features of six common freshwater species, Alburnus alburnus, Perca fluviatilis, Abramis brama, Rutilus rutilus, Scardinius erythrophthalmus, and Carassius carassius, and chemical parameters of water in the regulated ecosystem of the Styr River, Ukraine. Water quality was evaluated using biochemical oxygen demand (BOD5), chemical oxygen demand (COD), dissolved oxygen (DO), nutrients, solids, pH, and water quality classes (WQC). Meristic traits of fish were analyzed through frequency analysis of species (FAS) and the Zakharov scoring method (ZSM), while hierarchical cluster analysis (HCA) and neural networks (NN) were applied to detect associations between meristic traits of fish and water chemical parameters. Results revealed that overall water quality corresponded to WQC I–II (clean to moderately polluted), although COD consistently exceeded permissible limits. Key meristic traits, including fin rays, scales, and gill rakers, showed significant sensitivity to environmental variability, with species-specific responses reflecting ecological adaptation. The integrative use of WQC, FAS, ZSM, HCA, and NN demonstrated the potential of meristic traits to serve as reliable indicators of ecological integrity in freshwater systems.
Keywords:
fish morphology; bioindicators; water quality parameters; frequency analysis of species; morphological traits; meristic traits
Key Contribution: This study provides the first integrative application of WQC, FAS, ZSM, and NN to link fish morphological traits with chemical parameters of water quality.

Graphical Abstract

1. Introduction

Morphological meristic traits (MMTs) of fish have long been applied in taxonomy and systematics, yet their role as bioindicators of environmental quality has attracted growing attention in recent decades. These traits can reflect both species-specific adaptations and phenotypic responses to environmental stressors, thus offering a sensitive tool for ecological monitoring of freshwater ecosystems [1,2]. The significance of this approach lies in its ability to detect not only large-scale but also subtle habitat alterations that may remain unnoticed through conventional hydrochemical measurements. Importantly, the phenotypic variability of fish is shaped by the duration and intensity of stressors as well as by population-level genetic structures [3]. Case studies from different regions illustrate this phenomenon. For example, populations of Systomus sarana displayed notable morphological plasticity in India, with differences in body length and caudal peduncle height highlighting intraspecific heterogeneity [4]. Similarly, Schizothorax esocinus populations from rivers and lakes in Kashmir exhibited significant phenotypic divergence, as confirmed by morphometric correlations [5]. MMTs of fish, reflecting developmental instability, have also been proposed as a robust indicator of environmental stress [6,7]. MMT values are known to increase under anthropogenic pressures such as chemical contamination, pharmaceutical residues, or urbanization [7,8,9,10]. However, the literature remains divided: some studies suggest MMTs are reliable predictors of water quality, while others argue that population-level variation may obscure their diagnostic power, indicating the need for locally adapted assessment protocols [8,11]. Overall, previous research confirms that MMTs provide valuable ecological information, enabling the detection of both natural and anthropogenic impacts [12,13,14,15,16]. In our view, adapting this methodology to the specific conditions of lowland rivers in Ukraine holds considerable significance. From a scientific perspective, it represents a step forward in the development of a comprehensive methodology for linking fish morphological traits with hydrochemical parameters. At the same time, it has clear practical value, providing a foundation for improving water quality monitoring and supporting evidence-based management of aquatic ecosystems at the national level.
The aim of this study was to identify systematic associations between dominant morphological traits of six common fish species (Alburnus alburnus, Perca fluviatilis, Abramis brama, Rutilus rutilus, Scardinius erythrophthalmus, Carassius carassius) and chemical parameters of water quality in the regulated ecosystem of the Styr River, Ukraine (Figure 1a). We hypothesized that spatial variations in hydrochemical parameters of the Styr River—particularly those related to organic load (COD, BOD5) and nutrient enrichment (N–NH3, PO43−)—induce physiological and developmental stress in fish populations, leading to measurable changes in meristic traits, which are a subset of morphological traits. Specifically, oxygen depletion and elevated nitrogen compounds were expected to disrupt growth symmetry and proportionality in fin and scale structures, resulting in higher fluctuating asymmetry and altered body-form indices in species inhabiting impacted sites. Thus, morphological variability was assumed to serve as a biological integrator of environmental stress, complementing conventional physicochemical water quality indicators.
The objectives of this study were to (1) measure key chemical parameters (CPs): biochemical oxygen demand over 5 days (BOD5), chemical oxygen demand (COD), dissolved oxygen (DO), ammonium nitrogen (N–NH3), nitrite nitrogen (N–NO2), nitrate nitrogen (N–NO3), orthophosphate phosphorus (P–PO4),—Total Suspended Solids (TSS), Total Dissolved Solids (TDS), pH, and assign water quality classes (WQC); (2) characterize morphological meristic traits of fish, including fin rays, scales, and gill structures; (3) apply frequency analysis of species (FAS) and the Zakharov scoring method (ZSM) to detect dominant traits; (4) use hierarchical cluster analysis (HCA) and neural networks (NN) to assess associations between traits and hydrochemical conditions; and (5) identify bioindicator species and the most influential chemical parameters. This study provides the first integrative framework that combines WQC, FAS, ZSM, HCA, and NN to link MMTs with water quality parameters in a Ukrainian river ecosystem. Such a comprehensive approach not only advances our understanding of fish morphological responses to environmental stressors but also offers a methodological foundation for developing region-specific bioassessment tools for regulated river ecosystems.

2. Materials and Methods

The study included water sampling and fish catching, laboratory analysis of CPs, and assessment of MMTs of selected fish species (Figure 1b). The study does not consider comparing phylogenetic characteristics between species but focuses on assessing intraspecific changes under the influence of environmental conditions.

2.1. Study Area and Sampling Design

The study was conducted in the upper and middle reaches of the Styr River, a right tributary of the Prypiat River within the Dnieper basin, Ukraine [17]. Sampling sites were selected to represent contrasting hydrochemical and ecological conditions, including both relatively undisturbed areas and regions subject to anthropogenic influence (Figure 2). Site S1 was located between the Pidhaitsi and Boratyn settlements, immediately below the confluence with the Topillia River. This section represents an upstream reach with relatively minor human disturbance and was therefore considered as a reference site. Site S2 was situated within the city of Varash, downstream of the Horbakh River confluence, where the river is strongly influenced by municipal effluents and agricultural runoff. Site S3 was positioned further downstream in a stretch of the river bordered by extensive riparian forests and seasonally flooded vegetation, reflecting semi-natural conditions with only moderate anthropogenic pressures. Finally, Site S4 was located in the lower reach of the study area, immediately upstream of the confluence with the Styblya River, where the hydrochemical regime is affected by cumulative upstream influences, including industrial and energy-sector discharges. This spatial arrangement of sampling sites was designed to capture both reference and impacted conditions, thereby enabling the assessment of fish MMT gradients of ecological and hydrochemical variation.
Six locally abundant freshwater species, Alburnus alburnus, Perca fluviatilis, Abramis brama, Rutilus rutilus, Scardinius erythrophthalmus, and Carassius carassius (Table 1), were selected as indicator taxa. A total of 672 individuals were collected across four sampling sites (S1–S4) during the warm season of 2024 (May–October), yielding an approximately balanced dataset per site (ranging from 150 to 190 individuals). Within this total, each of the six species was represented by 22–35 individuals per site (see Appendix A, Table A1, Table A2, Table A3 and Table A4). This distribution ensured adequate coverage of both reference and impacted locations and allowed interspecific comparisons under variable hydrochemical conditions.

2.2. Chemical Parameters and Meristic Traits

Water samples were collected in parallel with fish captures at each sampling site, following the national standard [18]. The set of chemical parameters (CPs) selected for this study are widely recognized as indicators of freshwater ecosystem status (Table 2). Maximum permissible concentrations (MPC) were established according to Ukrainian standards [19,20].
The ecological assessment of surface water quality was carried out using the national methodology [31], which is based on comparing actual concentrations of CP with MPC values. Each parameter was then assigned a corresponding water quality class (WQC), ranging from class I (very clean water) to class V (very dirty water), providing an integrative ecological characterization of the aquatic environment (Table 3).

2.3. Meristic Traits of Fish

In this study, a set of MMTs was measured to characterize species-specific morphological variation (Figure 3). The values of all measurements of the right (R) and left (L) body parts of the fish, as well as the number of recorded asymmetric cases (A), are given in the appendices. The ratio of asymmetric manifestations of a trait was calculated as the ratio of the number of a specific trait (for the traits in Figure 3) to the total number of traits. Measurements were performed following the protocol described by [32], using a digital calliper (PowerMe TechMetric Pro, Dongguan, Guangdong, China) with an accuracy of ±0.1 mm. Two complementary approaches were applied for data analysis. The FAS method was used to evaluate the distribution and dominance of specific traits within and between fish populations, while the ZSM provided an integral assessment of dominant morphological features, allowing for interspecific comparisons. Together, these methods enable the identification of diagnostic traits and their sensitivity to environmental variability.

2.4. Neural Network Analysis and Statistical Methods

In this study, a NN model with three hidden layers was constructed to evaluate the associations between CPs and MMTs according to [33]. The NN model consisted of an input layer, three hidden layers, and an output layer. All continuous variables were normalized using the z-score transformation prior to model training to ensure comparable scaling and to improve convergence stability. The dataset was randomly divided into training (70%) and validation (30%) subsets. The model was trained using the backpropagation algorithm with a learning rate of 0.01, applying the hyperbolic tangent activation function in hidden layers and a linear activation function at the output. The load intensity of connections between nodes was categorized as follows: high (≤1.0), medium (≤0.75), moderate (≤0.5), and low (<0.25). To evaluate the structural importance of the NN components, node-level network metrics were calculated, which quantify how strongly each node contributes to the overall information flow within the trained network (Table 4).
HCA was applied to classify morphological traits and fish species, with clustering performed using linkage rules to group inherent trait characteristics and associations between CPs and MMTs [34]. HCA was performed using Euclidean distance and Ward’s minimum variance linkage method. The robustness of the resulting clusters was tested by multiscale bootstrap resampling, and approximately unbiased p-values > 95% were considered strong support for cluster stability. Density distributions were estimated using Gaussian kernel density estimation (KDE) with bandwidth selected by Silverman’s rule of thumb, and confidence regions shown on the scatterplots represent 95% kernel density contours [35]. Statistical analyses included calculation of the arithmetic mean (M), range (min–max), p-value threshold (p≤), and standard deviation (±SD). All statistical procedures, as well as NN and HCA analyses, were carried out using the JASP software package (Version 0.14.3) [36].

3. Results and Discussion

3.1. Chemical Parameters of Water Quality

Analysis of M and variations (±SD, min, max) of CPs provides an assessment of the ecological state of the aquatic environment and potential environmental risks (Table 5). The site-specific results are presented in Appendix B (Table A5). The concentrations of CPs show a gradient from relatively unchanged conditions upstream at point S1 to more affected environments downstream, particularly at points S2 and S4. The concentrations of BOD5 averaged 2.697 mg O2/L, remaining below the MPC threshold of 3.0 mg O2/L. In contrast, COD levels reached 35.333 mg O/L, more than double the MPC (15.0 mg O/L), suggesting the presence of persistent, slowly oxidizing organic compounds. Moreover, the high variability of COD (±14.975 mg/L) indicates pronounced fluctuations in organic load. DO concentrations averaged 10.207 mg O2/L, reflecting a favourable oxygen regime supportive of aquatic biota. Ammonium nitrogen (N–NH3) averaged 0.851 mg N/L, slightly below the MPC (1.0 mg N/L), although its relatively high standard deviation (±0.439 mg N/L) suggests notable variability. Nitrite nitrogen (N–NO2) remained very low (0.019 mg N/L), far below the MPC (0.1 mg N/L). Similarly, nitrate nitrogen (N–NO3) concentrations averaged 1.04 mg N/L, within permissible limits (2.0 mg N/L). Orthophosphate phosphorus (P–PO4) averaged 0.33 mg P/L, also below the MPC (0.5 mg P/L), indicating the absence of pronounced anthropogenic eutrophication. Overall, mean CP concentrations suggest compliance with ecological standards, with the exception of COD, which systematically exceeded the MPC and requires further investigation to identify sources of persistent organic pollutants. However, the maximum values of several CPs (BOD5, N–NH3, N–NO3, P–PO4) exceeded MPC thresholds, highlighting potential episodic inputs of organic and nutrient pollution.
The highest ecological quality (Figure 4) was demonstrated by DO, TDS, TSS, N–NO2, and P–PO4, whose average values corresponded to class WQC I (clean) and variability in classes 1–2, indicating well-oxygenated water with low mineral content. In contrast, parameters such as pH, BOD5, COD, N–NO3, and N–NH3 showed significant variability (min–max) in classes I–IV, while the average values corresponded to class II (moderately polluted). This pattern reflects the periodic influx of organic pollutants. Of particular concern are COD and N–NH3, which often reached the maximum values of class IV WQC (dirty), indicating systematic pollution pressure.
For the Styr basin, recent assessments highlight industrial and energy-sector pressures (e.g., power-plant discharges) and advocate integrated water management as part of a mitigation portfolio [37]. Although the present dataset focuses on conventional CPs, catchment management should consider co-occurring stressors that modulate organic nutrient dynamics and ecological response. From a WQC perspective, these patterns yield a composite status of class I–II (clean to moderately polluted) for most parameters, with organic load (COD) and reduced nitrogen (N–NH3) as the principal stressors that episodically push the system toward class IV. This aligns with recent work emphasizing the value of multi-metric organic pollution indices (e.g., OPI, saprobity-based metrics) to capture the temporal and compositional complexity of organic contamination beyond single-parameter thresholds; such indices can discriminate between biodegradable and refractory fractions and improve management prioritization [38,39]. This is directly pertinent to refining P–PO4 and N–NO3 trend detection in systems like the Styr [40].

3.2. Meristic Traits of Fish and Their Distribution

The analysis of meristic traits revealed pronounced variations across sampling sites (Figure 5). The number of traits on the right side of the fish body (R) demonstrated relatively wide distributions at all sites (S1–S4), with median values ranging between 15 and 25 and several outliers extending above 50. Although the interquartile ranges were comparable, S3 and S4 exhibited slightly broader variability than S1 and S2, suggesting higher morphological plasticity in fish from downstream locations. A similar pattern was observed for the left side of the body (L), where the distributions overlapped closely with those of R. The central tendency and spread of values were nearly symmetrical between the right and left sides, indicating that bilateral development in most individuals followed a consistent trajectory. Nevertheless, the occurrence of extreme values at S3 and S4 points to localized influences that may enhance morphological divergence. The number of asymmetric cases (A) showed the strongest spatial differentiation. Fish from S1 were characterized by low asymmetry, with values clustered around 5–7 cases and a narrow range. In contrast, S2 displayed the highest degree of asymmetry, with median values close to 15 and some individuals exceeding 20. The distributions at S3 and S4 were intermediate, but still considerably broader than at S1. Moreover, at S1, fish exhibited relatively low asymmetry and narrow trait distributions, indicating stable developmental processes under minimally disturbed conditions (Figure 5, Appendix B Table A5). By contrast, S2 demonstrated the highest degree of asymmetry and broader trait variability, which coincides with elevated COD and N–NH3 levels and suggests morphological stress responses to organic loading. Intermediate patterns were observed at S3 and S4, where asymmetry values were greater than at S1 but did not reach the levels recorded at S2, highlighting the cumulative but spatially moderated impact of downstream conditions.
To assess the informativeness of specific MMTs and to identify those that most effectively discriminate among fish groups, a comparative variability analysis was performed with respect to R, L, and A (Figure 6). Figure 6a–c highlights the strong linear relationship between R and L, confirming the bilateral correspondence of meristic traits across individuals. By contrast, associations between A and R or A and L revealed greater dispersion, reflecting species-specific and environmentally modulated variability. The highest variability was observed for the traits jj, jj.sk, and sp.br., all of which exhibited wide ranges and broader confidence intervals, suggesting their diagnostic potential in species separation. Conversely, squ.1, squ.2, and squ.pl showed relatively stable distributions, limiting their discriminative power in interspecific comparisons. The HCA supported these findings (Figure 6d). Species clustered into distinct groups based on their trait distributions, Abramis brama and Rutilus rutilus, displayed overlapping patterns in R and L but diverged in A, while Perca fluviatilis and Scardinius erythrophthalmus formed a separate group characterized by higher variability in asymmetry. Carassius carassius was clearly distinguished by broader distributions across all components, reflecting both interspecific differences and enhanced morphological plasticity. Taken together, these results indicate that R and L are informative primarily for assessing bilateral trait correspondence, whereas A provides greater sensitivity for detecting intraspecific variability and potential environmental influences.
Figure 7 illustrates the distribution of MMTs across the studied fish species, highlighting key structural patterns and ecological adaptations. Each species demonstrates dominant MMTs, with the most variable traits being P and V, which reflect locomotor performance and maneuverability, as well as jj and its sensory component jj.sk, which are linked to environmental changes. An increase in the number of gill rakers may indicate a dietary shift, whereas variation in the distribution of scales above and below the lateral line reflects adaptation to different water depths and flow conditions. Alburnus alburnus is characterized by high values of P, corresponding to elongated pectoral fins that enable rapid maneuvering. This species also exhibits numerous small scales (jj) and moderately developed pelvic fins (V). Perca fluviatilis shows pronounced values of V and jj.sk, pointing to a well-developed sensory system and effective swimming control, supported by broad pectoral fins for sudden attacks and relatively short pelvic fins. Abramis brama displays high values of P and jj, indicating a robust skeletal and integumentary system; the increased P contributes to efficient swimming in fast currents. Rutilus rutilus is distinguished by elevated values of f.br. and sp.br., traits associated with feeding adaptations, as they facilitate more efficient filtering of water during foraging. Scardinius erythrophthalmus demonstrates high values of V and jj.sk, suggesting adaptation to specific habitat conditions. Finally, Carassius carassius is defined by dominant traits P and jj, combined with an elevated dorsal profile (squ.1 > squ.2) which enhances buoyancy and supports life in stagnant waters (Figure 7). The multimodal KDE for MMTs—most notably sp.br. and jj.sk.—coincide with discrete specimen groups that hierarchical clustering separates on the basis of L and A. In other words, modes observed in the marginal density estimates align with cluster centroids in the L,R–A morphospace, indicating that discontinuities in MMTs are not random but systematically associated with contrasting body shape syndromes. Collectively, these results may support the interpretation that a taxonomic and interspecific structure is an ecologically driven variation that jointly shapes the dataset and is linked to habitat adaptation.
The ZSM analysis, which assigns integrated scores to each MMT depending on its value range, revealed species-specific morphological patterns among the examined fish (Figure 8). The dynamics of ZSM scores demonstrated that traits P and V tended to maintain consistently high levels (III–IV), indicating conservative morphogenetic stability within species. In contrast, gill-related traits (sp.br., f.br.) exhibited greater intra-specific variability, with most species showing dominance of intermediate scores (II–III). However, the increasing frequency of lower scores for gill traits may point to regressive morphophysiological changes. Traits associated with the lateral line (jj, jj.sk, squ.1, squ.2) showed less consistent integration. In populations exposed to fluctuating water chemistry, the distribution of dominant scores shifted toward lower ZSM categories, suggesting potentially delayed developmental processes in individuals. Both ZSM and FAS share a common principle, as they focus on identifying the frequency of specific MMTs within populations. While FAS emphasizes the statistical distribution of traits to determine dominant features, ZSM allows interspecific comparisons, thereby revealing species-specific characteristics. Notably, P and jj were identified as dominant variable traits in several species (Abramis brama, Perca fluviatilis, and Carassius carassius), likely reflecting shared ecological conditions. In contrast, Perca fluviatilis and Scardinius erythrophthalmus demonstrated different dominant traits, V and jj.sk, highlighting their distinctiveness and adaptive strategies. The ZSM and FAS share common principles, as both approaches focus on analyzing the frequency of MMTs within populations. FAS emphasizes the statistical distribution of traits to identify dominant features, whereas ZSM highlights interspecific comparisons, allowing the detection of species-specific characteristics. Dominant variable traits such as P and jj were commonly observed in several species (Abramis brama, Perca fluviatilis, and Carassius carassius), which may be linked to shared ecological conditions. In contrast, Perca fluviatilis and Scardinius erythrophthalmus were characterized by different dominant traits, V and jj.sk, reflecting their distinctiveness and possible adaptive strategies.
The MMTs jj, jj.sk, and sp.br. exhibited the highest variability, indicating their potential as key diagnostic features reflecting ecological and functional differentiation. In contrast, relatively stable traits such as squ.1, squ.2, and squ.pl proved of limited diagnostic utility—consistent with the notion that some meristic counts remain conservative across environmental contexts. These findings align with broader MMT fish research. For instance, studies investigating Salvelinus sp. populations demonstrated that polymorphism—especially in traits related to resource utilization and habitat—facilitates coexistence and reflects adaptive divergence [41]. Likewise, traditional MMT approaches remain invaluable for distinguishing fish populations and stock structures [42,43]. Density distribution analyses revealed asymmetry and multimodality in sp.br. and jj.sk, suggesting the presence of distinct morphotypes or adaptive forms in sample. Clear, non-overlapping density regions between groups imply these metrics could reliably differentiate between species or phenotypic variants. This observation resonates with adaptive radiation studies where feeding-related morphological traits diverge among sympatric morphs—such as in Telmatherina species in Lake Matano, where divergence is strongest in traits linked to feeding [44].

3.3. Identifying the Loads of the Dominant Formation Model

To evaluate the structural associations between environmental characteristics and fish MMTs in the Styr River, a NN approach was applied. The analysis of NN node importance (Figure 9a) identified the highest scores for water quality and CPs, particularly DO and COD. Among the MMT indicators, the integral nodes 1M and 2M—representing the ZSM and FAS assessments, respectively—showed the greatest importance, underscoring their indicative role in linking CPs with fish morphology. The results of HCA, performed to classify CPs and MMTs based on NN node importance, revealed four distinct clusters that describe the interaction structure between environmental and morphological variables (Figure 8b). Cluster 1 included DO, COD, BOD5, WQC, and pH. These variables had the highest centrality scores, forming the network core and reflecting the oxygen regime together with overall water quality. Cluster 2 comprised solid residue indicators (TDS, TSS) and biogenic substances (N–NO3, N–NO2, N–NH3, P–PO4). These variables collectively represent chemical loading in the river and demonstrated significant correlations with DO, COD, and pH, highlighting their joint role in modelling anthropogenic pressure. Cluster 3 grouped the morphological traits (L, A, P, V, jj, jj.sk, f.br., sp.br., squ.pl, squ.1, squ.2). Although these traits exhibited relatively lower centrality within the network, they were characterized by strong internal connectivity, indicating consistent patterns of morphological response to environmental conditions. Finally, Cluster 4 included the integrative indicators 1M and 2M. Positioned between chemical and morphological parameters, these traits act as composite bioindicators, capturing both chemical stressors and biological responses within the aquatic ecosystem.
Analysis of node load (Figure 10) allowed us to identify the most influential variables that determine the network structure and provide inter-node connections between the environment and the organism, while the lines show partial correlations between two variables independent of all other variables, with thicker/darker lines corresponding to higher values. Within the neural network, node 1M—reflecting the ZSM-based integrated assessment of MMTs—serves as a critical element. This node has the highest values of strength, connectivity, and proximity, which indicates its functioning as an integrator in the reflection of MMT.
The second important node is 2M (Figure 10), which is based on the integral FAS indicator, which is also associated with many morphological features and has high load values, but is slightly inferior to 1M in terms of influence. The integral indicator WQC, which reflects the ecological quality of water according to CP, also occupies one of the central places in the NN model. It has the largest number of direct links with CP. It is through WQC that numerous chains of influence on fish MMTs are formed. The oxygen regime and organic pollution indicators DO, BOD5, and COD have a significant load in the NN model. These parameters have high values of strength and expected impact, which indicates their direct impact on MMTs. For example, the indicators sp.br., f.br., and jj show strong links with DO and WQC, indicating an adaptive response of the gill and sensory apparatus of fish to changes in the oxygen regime of water. The jj is largely a species-specific meristic character and is typically stable within populations; observed differences in jj among our samples primarily reflect interspecific variation rather than plastic responses to short-term environmental change. Changes in jj are more likely associated with developmental/genetic differences or physical scale loss. Similar studies show that low DO levels are associated with reduced growth rates, skeletal deformities, and altered scales [45]. At the same time, biogenic substances (N–NO3, N–NO2, N–NH3, P–PO4) and mineral components (TDS, TSS) have weaker correlations, but their indirect influence through oxygen imbalance is confirmed by their involvement in inter-node pathways. The acid–base balance (pH) indicator is characterized by a lower load but is important because it maintains the acid–base reactions of the environment; in particular, it is associated with the formation of the structure of fins (P, V) and scales (squ.1, squ.2, squ.pl). These MTs respond more slowly and to a lesser extent to short-term changes but still show stable associations. However, an increase in biogenic substances (in particular N–NH3) can lead to hypoxia and disruption of gill structure formation [46], while pH and salt load affect the formation of scales and fins, especially in the early stages of development [47].
From a broader perspective, the obtained NN model highlights the integrative role of oxygen regime and WQC as central mediators linking environmental stressors to fish morphology. These findings align with previous studies on hydroecosystem stability, where oxygen availability, organic pollution, and biofouling were identified as critical regulators of aquatic homeostasis [48]. Considerable loadings were observed for the parameters of the oxygen regime and organic pollution (DO, BOD5, COD), which displayed high strength and expected influence [49,50]. DO is a fundamental indicator of aquatic ecosystem status, as it governs metabolic processes, distribution patterns, and survival thresholds of aquatic organisms. Within the analyzed parameters, DO showed the strongest influence, highlighting its pivotal role in determining both water quality and biological responses [51] (Figure 11). DO solubility declines with rising temperature, thereby reducing availability and increasing physiological stress for fish and invertebrates. In addition, hydrostatic pressure and oxygen transfer characteristics, including bubble size and surface area, regulate dissolution efficiency and subsequent bioavailability. The ecological relevance of these processes is evident in the species-specific minimum DO requirements, which range from relatively tolerant organisms to highly sensitive taxa such as striped bass. Similarly, in Lake Sevan the main cause of fish deterioration was identified as reduced DO combined with elevated organic load (BOD, COD, NH4+) and stagnant water, while major elements and metals played no significant role in biochemical responses [52]. Moreover, the study in [53] likewise demonstrated that chemical parameters, particularly trace elements, showed significant correlations with physiological and biochemical indicators, supporting the consistency of such relationships with our findings. Thus, the oxygen regime functions simultaneously as a key physicochemical parameter and as a biological filter, shaping community composition, habitat suitability, and the resilience of aquatic ecosystems. Nutrients (N–NO3, N–NO2, N–NH3, P–PO4), TDS, and TSS exhibited weaker direct connections; however, their indirect influence through disruption of the oxygen balance was confirmed by their inclusion in inter-node pathways.
CP water determines a chain of related changes in fish reactions: from behavioural changes (stress, avoidance, decreased feeding and orientation activity) through physiological changes (hypoxia, metabolic disorders, endocrine disruptions, tissue damage) to defects manifested in the growth of MMT asymmetry (Figure 12a). Thus, ZSM and FAS can be considered as integral indicators of ecological stress in aquatic ecosystems caused by anthropogenic chemical loads. DO concentration is a factor that determines fish behaviour (Figure 12b). Its deficiency causes characteristic reactions: rising to the surface of the water body and decreased foraging activity. At the physiological level, an energy deficit, lactate accumulation, and acidosis occur, leading to slowed growth and violations of MMTs [51,54]. An increase in BOD5 is accompanied by a decrease in DO concentration, which causes avoidance of polluted areas, reduced feeding activity, and impaired orientation of larvae. The physiological consequences are secondary hypoxia, accumulation of organic decomposition products, and activation of stress hormones, which limit growth and development [55]. This leads to impaired symmetrical fin formation, reflected in the growth of MMT asymmetry. High COD values are associated with the presence of toxic organic and inorganic impurities. Fish experience disorientation, apathy, and reduced social interaction; physiologically, this can cause damage to the gills and liver, oxidative stress, and protein synthesis disorders [56]. This causes abnormalities in the development of scales and fins. N–NH3, N–NO2, and N–NO3 exhibit different mechanisms of toxicity. N–NH3 causes internal hypoxia and gill damage, N–NO2 oxidizes hemoglobin to methaemoglobin, and N–NO3 causes endocrine disorders and oxidative stress when exposed chronically [57,58]. The corresponding physiological changes lead to asymmetrical development of the gill arches, fins, and skeleton. P–PO4 mainly has an indirect effect through the stimulation of eutrophication and nocturnal hypoxia due to the mass decomposition of phytoplankton [59]. TSS reduce water transparency, limiting visual orientation and feeding. They also mechanically irritate the gills, increasing the energy expenditure for breathing, which, in case of prolonged exposure, manifests itself in asymmetry in the development of the head and fins [60]. Significant pH fluctuations cause behavioural changes (rubbing against the bottom, jumping, disorientation) and are accompanied by ion exchange disorders, such as increased toxicity of metals in an acidic environment and ammonium in an alkaline environment [61].
Analysis of fish MMTs revealed differences in the dominance of structures reflecting the sensitivity of different species to CPs (Figure 12c). In Abramis brama and Carassius carassius, changes in the fin trait P and sensory element jj predominate, indicating their response primarily to organic load (COD, N–NH3). For Perca fluviatilis, two groups of dominant traits were recorded: P, jj and V, jj.sk. This indicates a dual type of sensitivity to both organic pollution and oxygen regime. Such a reaction emphasizes its ecological plasticity and ability to reflect the combined action of polluting factors. Scardinius erythrophthalmus shows changes in the structure of V and the sensory indicator jj.sk, which is associated with the influence of organic load. In contrast, Rutilus rutilus is characterized by variability in gill traits (f.br., sp.br.), which directly depend on the oxygen regime. The balanced representation of species across sampling sites and the adequate sample size enhanced the robustness of interspecific and spatial comparisons, reducing potential bias due to uneven sampling effort.
At the same time, Ukraine’s national and local programmes for the protection of aquatic biological resources are aimed at protecting and restoring fish fauna, improving the condition of water bodies, and establishing a system for monitoring the state of aquatic ecosystems and their biological components [62,63]. This situation demonstrates the need for wider implementation of accessible and scientifically sound bioindication methods based on the analysis of local fish species parameters, which allow for rapid assessment of the state of aquatic ecosystems and response to negative changes. The results of the presented study can be considered as a pilot project for accumulating real data on the MMT of local fish species in the Dnieper basin, on the basis of which it is possible to indicate the ecological conditions of the aquatic environment of rivers [64]. Several Ukrainian studies lend support to the notion that fish morphology responds sensitively to hydrological and chemical–environmental gradients. For example, study [65] developed a model of water quality in the Sluch, Ustya, and Styr rivers based on fluctuating asymmetry in fish populations. A study [48] demonstrated that the morphological variability of Perca fluviatilis correlated significantly with hydrochemical parameters in the Ukrainian river system. Another study [66] demonstrated morphological variability in Abramis brama in the Dnieper–Bug estuary due to anthropogenic regulation of flow. These works provide a basis to interpret our own findings: MMT patterns that we observed across species and stations likely reflect the combined influence of water chemistry. The spatial patterns in meristic traits of fish observed in the Styr River, particularly the elevated variability of gill and sensory features (jj, jj.sk, sp.br.) under higher COD and N–NH3 levels, are consistent with evidence from other freshwater systems where morphological asymmetry reflects environmental stress. Comparable relationships between fluctuating asymmetry and pollutant exposure have been reported for fish otoliths and skeletal structures in Chinese and North American rivers [67,68] (Yi et al., 2024; Arch. Environ. Contam. Toxicol., 2015). Such findings reinforce the notion that morphological endpoints integrate the cumulative effects of hydrodynamic, chemical, and habitat stressors that may be under-represented in routine water chemistry monitoring. Such findings reinforce the notion that morphological endpoints integrate the cumulative effects of hydrodynamic, chemical, and habitat stressors that may be under-represented in routine water chemistry monitoring. Methodologically, our approach aligns with the principles outlined by [69], who emphasized the diagnostic value of fluctuating asymmetry in geometric morphology, although we applied a meristic framework rather than landmark-based shape analysis. The asymmetry indices, when interpreted with attention to sampling design and population structure, provide reliable indicators of developmental instability in fishes exposed to exogenous stress [70]. Overall, the concordance of our results with these international studies confirms that MMT indicators can serve as cost-effective, organism-level complements to biochemical and physicochemical metrics in ecological monitoring. Moreover, our data extend these concepts to Ukrainian lowland rivers, demonstrating that the meristic responses of native species in the Styr basin follow patterns similar to those documented in other temperate systems affected by organic and nutrient pollution. The proposed approach can be scaled to other regions, provided that the methodology is refined by adjusting the model to local conditions. This sequence of actions will ensure the reliability and universality of fish MMT indicators, which will subsequently allow them to be integrated into the national environmental monitoring system and will also be a reliable tool for the sustainable management of aquatic biological resources.

4. Conclusions

This study provides empirical evidence that fish MMTs systematically respond to gradients of CPs in their aquatic environment, highlighting their potential as sensitive bioindicators. The findings showed that the Styr River, based on most CPs, corresponds to WQC I–II and is generally characterized as clean to moderately polluted. The most influential factors affecting water quality were COD and N–NH3 concentrations. Among MMTs, the greatest variability was observed in sensory scales and gill traits (jj, jj.sk, and sp.br.), whereas lateral line and caudal fin characteristics (squ.1, squ.2, and squ.pl) exhibited stable values, suggesting their limited diagnostic value for detecting associations. The jj should be considered primarily a taxonomic and species trait; any interpretation of environmental sensitivity requires further targeted study. Density distribution patterns revealed asymmetry and multimodality in gill (sp.br.) and sensory scale (jj.sk) traits. Species-specific differences were also evident: Abramis brama, Perca fluviatilis, and Carassius carassius were dominated by P and jj; Perca fluviatilis and Scardinius erythrophthalmus by V and jj.sk; and Rutilus rutilus by f.br. and sp.br. Traits P and V showed conservative morphogenetic stability within species, while gill-related traits (sp.br., f.br.) exhibited greater intra-specific variability. Systematic associations between chemical and morphological parameters were identified. Dominant CPs influencing MMTs included oxygen regime—linked to reductions in sensory scales (jj.sk)—and organic pollution, which was associated with changes in fin structures (P and V). Both FAS and ZSM confirmed adaptive differentiation of fish species by MMTs. Importantly, the analysis indicated high sensitivity to organic pollution in Abramis brama, Perca fluviatilis, Carassius carassius, and Scardinius erythrophthalmus. The integrative approach combining WQC, FAS, ZSM, and NN demonstrates the utility of MTs as quantitative indicators for assessing ecological integrity and detecting anthropogenic impacts in freshwater ecosystems. With appropriate regional calibration, the morphological indicators identified in this study can be applied for ecological monitoring in other rivers of the Dnieper basin for the sustainable management of aquatic biological resources in Ukraine.

Author Contributions

Conceptualization, O.B., P.K. and B.G.; methodology, J.A. and A.P.; software, P.K. and V.K.; validation, Y.G. and S.K.; formal analysis, B.G.; investigation, O.B., V.K. and A.P.; resources, J.A.; data curation, O.B.; writing—original draft preparation, all authors; writing—review and editing, all authors; visualization, P.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was carried out within the framework of an international grant project EU Erasmus+ No101082557 AFISHE Development of Aquaculture and Fisheries Education for Green Deal in Armenia and Ukraine: from education to ecology.

Institutional Review Board Statement

In accordance with Royal Decree 53/2013 and European Directive 2010/63/EU concerning the protection of animals used for scientific research, in which invertebrates are not included, official approval was not required. There is no need to have Institutional Review Board approval.

Informed Consent Statement

Not applicable.

Data Availability Statement

MMT data presented in this study are available in Appendix A. Water quality data at the sampling sites are presented in Appendix B. Additional CP data are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
1MIntegrated indicator of meristic traits based on ZSM
2MIntegrated indicator of meristic traits based on FAS
ANumber of asymmetric cases
BC(v)Betweenness centrality
BOD5Biochemical oxygen demand (over 5 days)
CC(v)Closeness centrality
CODChemical oxygen demand
CPChemical parameter
D(v)Degree (number of direct links of a node)
DODissolved oxygen
Dw(v)Weighted degree (strength of connections)
f.br.Number of gill filaments
FASFrequency analysis of species
HCAHierarchical cluster analysis
jjNumber of scales on the lateral line
jj.skNumber of scales with sensory canals (lateral line system)
KDEGaussian kernel density estimation
LNumber of traits on the left side of the fish’s body
MMean
MMTMorphological meristic trait
MPCMaximum permissible concentration
NNNeural network
N–NH3Ammonium nitrogen
N–NO2Nitrite nitrogen
N–NO3Nitrate nitrogen
PPectoral fin rays
P–PO4Orthophosphate phosphorus
RNumber of traits on the right side of the fish’s body
±SD–Standard deviation
sp.br.Number of gill rakers on the first gill arch
squ.1Number of scales above the lateral line
squ.2Number of scales below the lateral line
squ.plNumber of rays on the caudal fin
TDSTotal dissolved solids
TSSTotal suspended solids
VPelvic fin rays
WQCWater quality class
ZSMZakharov scoring method
p≤p-value threshold

Appendix A

Table A1. MMT values * collected from the Styr River at site S1.
Table A1. MMT values * collected from the Styr River at site S1.
MMTAlburnus alburnus
(n = 34)		Rutilus rutilus
(n = 29)		Scardinius erythrophthalmus
(n = 27)		Perca fluviatilis
(n = 31)		Carassius carassius
(n = 28)		Abramis brama
(n = 23)
RLARLARLARLARLARLA
PM10.610.81716.616.51110.710.61713.713.71112.912.9516.916.87
±SD0.490.440.560.570.450.490.480.460.360.360.290.43
p≤0.050.050.050.050.050.050.050.050.010.01-0.01
VM7.67.6168.78.61213.713.61110.810.51611.911.888.98.99
±SD0.490.490.480.490.480.490.440.510.360.420.290.32
p≤0.050.050.050.050.050.050.050.050.010.05--
sp.br.M45.044.82411.111.31745.245.42151.151.02352.352.31323.823.915
±SD0.890.80.820.740.820.720.830.750.810.710.50.24
p≤0.050.050.050.050.050.050.050.050.050.050.050.05
f.br.M9.59.662.62.51110.510.51611.511.7812.712.642.92.86
±SD0.590.580.550.570.570.640.510.480.480.500.290.43
p≤0.050.050.050.050.050.050.050.050.050.05-0.01
jjM45.545.7941.841.61237.837.61668.768.61540.840.8552.952.99
±SD0.650.480.420.490.370.490.460.500.390.390.350.35
p≤0.050.050.050.050.010.050.050.050.010.01--
jjcкM18.718.71040.840.61142.842.81260.860.71138.938.8452.852.85
±SD0.460.460.440.490.370.430.440.480.360.420.40.43
p≤0.050.050.050.050.010.050.050.050.010.050.010.01
squ.1M10.910.896.96.899.99.947.87.8109.08.948.99.05
±SD0.20.440.340.400.250.340.380.380.00.320.290.0
p≤-0.050.050.05-0.010.010.01----
squ.2M4.03.873.83.973.93.954.94.9104.94.933.93.93
±SD0.00.410.370.340.250.30.200.280.320.20.210.35
p≤-0.010.050.05--------
squ.plM12.011.9410.810.9812.912.9311.811.7911.911.9312.912.92
±SD0.00.280.370.300.250.30.420.460.190.260.290.29
p≤--0.050.05--0.050.05----
Table A2. MMT values * collected from the Styr River at site S2.
Table A2. MMT values * collected from the Styr River at site S2.
MMTAlburnus alburnus
(n = 29)		Rutilus rutilus
(n = 35)		Scardinius erythrophthalmus
(n = 31)		Perca fluviatilis
(n = 23)		Carassius carassius
(n = 27)		Abramis brama
(n = 28)
RLARLARLARLARLARLA
PM10.7510.671816.7616.711710.8210.681413.8213.71812.8412.89716.6816.3415
±SD0.440.480.440.460.390.480.390.470.380.320.470.49
p≤0.050.050.010.050.010.05-0.01--0.050.05
VM7.717.75158.768.761813.8213.77810.7110.77712.011.7958.778.3416
±SD0.460.440.440.440.390.430.470.440.000.420.430.49
p≤0.050.050.010.010.010.010.010.01-0.010.050.05
sp.br.M45.044.921910.7610.912545.2345.182051.2950.941752.5852.371124.1822.6421
±SD1.181.141.261.091.11.051.11.090.901.121.051.34
p≤0.050.050.050.050.050.050.010.050.010.010.050.05
f.br.M9.589.6752.9182.8699.829.77311.8211.82612.9512.9532.912.828
±SD0.540.480.300.360.390.430.390.390.230.230.290.39
p≤0.050.05--0.010.01-----0.01
jjM45.7945.671641.8141.811241.8241.591367.8867.82540.8940.84452.8252.867
±SD0.420.480.400.400.390.670.330.390.320.380.390.35
p≤0.010.050.010.010.010.05----0.01-
jjcкM16.7916.581340.6740.67741.8241.771360.8860.88638.8438.79352.9152.866
±SD0.420.540.480.480.390.430.330.330.500.540.290.35
p≤0.010.050.050.050.010.01------
squ.1M10.5810.7186.717.6759.739.73107.887.8248.798.7438.958.914
±SD0.540.460.460.480.460.460.330.390.420.450.210.29
p≤0.050.050.050.050.050.05--0.010.01--
squ.2M3.713.5853.673.7153.773.6884.774.6534.794.7423.953.913
±SD0.460.540.480.460.430.480.440.490.420.450.210.29
p≤0.050.050.050.050.010.050.010.050.010.01--
squ.plM11.7111.7949.719.67512.7712.73711.7111.77411.7911.74212.9513.952
±SD0.460.420.460.480.430.460.470.440.420.450.210.21
p≤0.050.010.050.050.010.050.010.010.010.01--
Table A3. MMT values * collected from the Styr River at site S3.
Table A3. MMT values * collected from the Styr River at site S3.
MMTAlburnus alburnus
(n = 27)		Rutilus rutilus
(n = 32)		Scardinius erythrophthalmus
(n = 34)		Perca fluviatilis
(n = 25)		Carassius carassius
(n = 22)		Abramis brama
(n = 29)
RLARLARLARLARLARLA
PM10.5910.682116.7416.651810.6210.671916.6813.841212.7712.71916.7616.8111
±SD0.500.480.450.490.500.480.480.380.440.470.440.40
p≤0.050.050.050.050.050.050.050.010.010.010.010.01
VM7.647.68208.618.611610.5710.521610.6810.741612.7712.7788.818.8112
±SD0.490.480.500.500.600.680.480.450.560.440.400.40
p≤0.050.050.050.050.050.050.050.01-0.010.010.01
sp.br.M44.7644.282310.8911.022444.9544.672250.7351.022052.051.821024.0923.7218
±SD1.181.211.070.091.021.161.091.020.941.131.141.17
p≤0.050.050.050.050.050.050.050.050.050.050.050.05
f.br.M9.719.67152.682.68179.579.621410.8410.84512.7712.8268.818.815
±SD0.460.480.480.480.510.500.380.380.440.390.400.40
p≤0.050.050.050.050.050.050.010.010.01-0.010.01
jjM45.7145.711241.6141.651241.5241.34967.7467.681040.7740.82452.5452.6510
±SD0.460.460.500.490.510.570.450.480.440.390.380.42
p≤0.050.050.050.050.050.050.010.050.01-0.010.01
jjcкM16.7116.59940.6540.651241.5741.48560.7460.68738.7738.77352.6552.546
±SD0.460.500.490.490.510.510.450.480.440.440.420.38
p≤0.050.050.050.050.050.050.010.010.010.010.010.01
squ.1M10.6810.6866.616.61109.679.5767.687.6868.828.7728.628.626
±SD0.480.480.500.500.480.510.480.480.390.440.500.50
p≤0.050.050.050.050.050.050.050.05-0.010.050.05
squ.2M3.683.6853.613.6193.623.5244.684.6854.824.8223.713.715
±SD0.480.480.500.500.500.510.480.480.390.390.460.46
p≤0.050.050.050.050.050.050.050.05--0.050.05
squ.plM11.6411.6468.618.61812.6212.52311.6811.68411.8211.82112.7112.715
±SD0.490.490.500.500.500.510.480.480.390.390.460.46
p≤0.050.050.050.050.050.050.050.05--0.050.05
Table A4. MMT values * collected from the Styr River at site S4.
Table A4. MMT values * collected from the Styr River at site S4.
MMTAlburnus alburnus
(n = 27)		Rutilus rutilus
(n = 29)		Scardinius erythrophthalmus
(n = 33)		Perca fluviatilis
(n = 25)		Carassius carassius
(n = 23)		Abramis brama
(n = 21)
RLARLARLARLARLARLA
PM10.7410.74916.5316.58610.7710.77813.7713.82812.7512.75416.6716.59
±SD0.450.450.510.510.440.440.440.390.450.450.490.51
p≤0.010.010.050.050.010.010.010.010.010.010.050.05
VM10.6810.63118.478.53713.7713.77610.7110.771112.3812.1368.788.5611
±SD0.580.500.510.510.440.440.470.440.620.720.430.51
p≤0.010.050.050.050.010.010.010.010.050.050.010.05
sp.br.M45.1144.841511.0310.87945.0644.941050.8250.881452.4452.191123.9423.7815
±SD1.051.210.981.011.091.091.021.271.091.051.111.17
p≤0.050.050.050.050.050.050.050.050.010.050.050.05
f.br.M9.689.6852.472.4739.719.65411.8211.77612.8812.8852.942.896
±SD0.480.480.510.510.470.490.390.440.340.340.240.32
p≤0.050.050.050.050.010.050.010.01----
jjM45.6845.63841.5341.53641.5341.59667.7167.65934.8840.81652.6152.4411
±SD0.480.500.510.510.510.510.470.490.340.400.500.51
p≤0.050.050.050.050.050.050.050.05--0.050.05
jjcкM16.6316.53740.4240.37541.4741.47660.7160.71738.7538.75452.6152.59
±SD0.500.520.510.500.510.510.470.470.450.450.500.51
p≤0.050.050.050.050.050.050.010.010.010.010.050.05
squ.1M10.4710.4766.426.3749.479.5937.777.7168.638.6348.678.58
±SD0.510.510.510.500.510.510.440.470.500.500.490.51
p≤0.050.050.050.050.050.050.010.010.050.050.050.05
squ.2M3.473.5353.423.3743.473.5944.774.7164.634.6333.723.57
±SD0.510.510.510.500.510.510.440.470.500.500.460.51
p≤0.050.050.050.050.050.050.010.010.050.050.010.01
squ.plM11.4711.5359.849.98412.4712.53411.7711.71511.6311.56312.7212.56
±SD0.510.510.380.210.510.510.440.470.500.510.460.51
p≤0.050.050.010.010.050.050.010.010.050.050.010.01
* Note. Abbreviations used in Table A1, Table A2, Table A3 and Table A4: MMT—morphological meristic trait; M—mean; ±SD—standard deviation; p≤—p-value threshold; P—pectoral fin rays; V—pelvic fin rays; sp.br.—gill rakers on the first gill arch; f.br.—gill filaments; jj—lateral line scales; jj.sk—lateral-line scales with sensory canals; squ.1—scales above the lateral line; squ.2—scales below the lateral line; squ.pl—caudal fin rays; n—number of individuals examined; R—number of traits on the right side of the fish’s body; L—number of traits on the left side of the fish’s body; A—number of asymmetric cases.

Appendix B

Table A5. Results * for the chemical parameters of water quality at sites S1, S2, S3, S4 (2024).
Table A5. Results * for the chemical parameters of water quality at sites S1, S2, S3, S4 (2024).
CPM±SDMinMaxM±SDMinMax
Site: S1Site: S2
BOD5, mg O2/L3.060.122.903.152.590.102.502.70
COD, mgO/L33.582.5031.0036.0033.583.0030.0037.00
DO, mg O2/L9.750.309.4010.1010.730.3510.3011.10
N–NH3, mgN/L0.350.020.330.370.280.030.250.32
N–NO2, mgN/L0.0150.0020.0130.0170.0200.0020.0170.023
N–NO3, mgN/L1.440.151.251.600.980.120.851.10
P–PO4, mgP/L0.280.030.250.320.340.040.300.39
TSS, mg/L9.150.508.609.709.390.409.009.80
pH, units8.340.058.288.388.270.048.228.31
Site: S3Site: S4
BOD5, mg O2/L3.050.132.903.203.010.112.903.15
COD, mgO/L47.165.0042.0053.0041.084.0036.0046.00
DO, mg O2/L9.760.259.5010.109.760.309.4010.10
N–NH3, mgN/L0.280.020.260.310.3460.0300.3100.380
N–NO2, mgN/L0.0150.0020.0130.0180.0130.0020.0110.016
N–NO3, mgN/L1.060.150.901.250.650.090.550.75
P–PO4, mgP/L0.230.030.200.270.360.040.320.41
TSS, mg/L9.150.408.709.6011.070.5010.5011.60
pH, units8.390.058.338.448.260.048.218.30
* Note. Abbreviations used in Table A5: M—mean; ±SD—standard deviation; min—minimum; max—maximum.

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Figure 1. The aim, objectives (a), and stages of this study (b).
Figure 1. The aim, objectives (a), and stages of this study (b).
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Figure 2. Schematic representation of sampling sites, catches, and fish species studied.
Figure 2. Schematic representation of sampling sites, catches, and fish species studied.
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Figure 3. List of meristic traits used in the study.
Figure 3. List of meristic traits used in the study.
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Figure 4. Water quality classes for chemical parameters of the Styr River.
Figure 4. Water quality classes for chemical parameters of the Styr River.
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Figure 5. Results of distribution between meristic traits on the right side of the fish (a), on the left side of the fish (b), and the number of asymmetrical cases (c) of fish at different fishing sites S1–S4 of the Styr River.
Figure 5. Results of distribution between meristic traits on the right side of the fish (a), on the left side of the fish (b), and the number of asymmetrical cases (c) of fish at different fishing sites S1–S4 of the Styr River.
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Figure 6. Gaussian kernel density estimation plots for meristic traits (top marginal plots show univariate kernel densities) (a–c) and hierarchical cluster analysis results of species (d).
Figure 6. Gaussian kernel density estimation plots for meristic traits (top marginal plots show univariate kernel densities) (a–c) and hierarchical cluster analysis results of species (d).
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Figure 7. Comparison of meristic traits across fish species using frequency analysis of species: (a) pectoral fin rays P, (b) pelvic fin rays V, (c) number of gill rakers on the 1st gill arch sp.br., (d) number of gill filaments f.br., (e) number of lateral line scales jj, (f) number of lateral-line scales with sensory canals jj.sk, (g) number of scales above lateral line squ.1, (h) number of scales below lateral line squ.2, (i) number of caudal fin rays squ.pl.
Figure 7. Comparison of meristic traits across fish species using frequency analysis of species: (a) pectoral fin rays P, (b) pelvic fin rays V, (c) number of gill rakers on the 1st gill arch sp.br., (d) number of gill filaments f.br., (e) number of lateral line scales jj, (f) number of lateral-line scales with sensory canals jj.sk, (g) number of scales above lateral line squ.1, (h) number of scales below lateral line squ.2, (i) number of caudal fin rays squ.pl.
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Figure 8. Comparison of meristic traits across fish species based on Zakharov scoring method analysis: (a) pectoral fin rays P, (b) pelvic fin rays V, (c) number of gill rakers on the 1st gill arch sp.br., (d) number of gill filaments f.br., (e) number of lateral line scales jj, (f) number of lateral-line scales with sensory canals jj.sk, (g) number of scales above lateral line squ.1, (h) number of scales below lateral line squ.2, (i) number of caudal fin rays squ.pl.
Figure 8. Comparison of meristic traits across fish species based on Zakharov scoring method analysis: (a) pectoral fin rays P, (b) pelvic fin rays V, (c) number of gill rakers on the 1st gill arch sp.br., (d) number of gill filaments f.br., (e) number of lateral line scales jj, (f) number of lateral-line scales with sensory canals jj.sk, (g) number of scales above lateral line squ.1, (h) number of scales below lateral line squ.2, (i) number of caudal fin rays squ.pl.
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Figure 9. Metrics of the importance of the neural networks node (a) and dendrogram of results of hierarchical cluster analysis; (b) chemical parameters and meristic traits fish the Styr River.
Figure 9. Metrics of the importance of the neural networks node (a) and dendrogram of results of hierarchical cluster analysis; (b) chemical parameters and meristic traits fish the Styr River.
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Figure 10. Estimation of the dominant influence model chemical parameters and morphological traits of fish the Styr River using neural networks analysis.
Figure 10. Estimation of the dominant influence model chemical parameters and morphological traits of fish the Styr River using neural networks analysis.
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Figure 11. Factors influencing dissolved oxygen on aquatic organisms.
Figure 11. Factors influencing dissolved oxygen on aquatic organisms.
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Figure 12. Chain of influence of chemical indicators on behavioural changes and physiological processes (a), formation of changes in meristic traits (b), and species-specific sensitivity (c).
Figure 12. Chain of influence of chemical indicators on behavioural changes and physiological processes (a), formation of changes in meristic traits (b), and species-specific sensitivity (c).
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Table 1. Summary information on the most common local fish species in the upper and middle reaches of the Styr River.
Table 1. Summary information on the most common local fish species in the upper and middle reaches of the Styr River.
OrderFamilySpeciesEnglish NameIUCN 1 StatusNumber
CypriniformesCyprinidaeCarassius carassius Linnaeus, 1758Crucian carpLeast Concern100
CypriniformesLeuciscidaeScardinius erythrophthalmus Linnaeus, 1758RuddLeast Concern125
CypriniformesLeuciscidaeAlburnus alburnus Linnaeus, 1758BleakLeast Concern117
CypriniformesLeuciscidaeRutilus rutilus Linnaeus, 1758RoachLeast Concern125
CypriniformesLeuciscidaeAbramis brama Linnaeus, 1758Freshwater breamLeast Concern101
PerciformesPercidaePerca fluviatilis Linnaeus, 1758European perchLeast Concern104
Note: 1 Status of fish according to the classification of the International Union for Conservation of Nature (IUCN).
Table 2. List of chemical parameters analyzed, methods of measurement, and MPC [19,20].
Table 2. List of chemical parameters analyzed, methods of measurement, and MPC [19,20].
CPMeasurement MethodMPC Value
BOD5[21]≤3.0 mg O2/L
COD[22]≤15.0 mg O2/L
DO[23]≥5.0 mg O2/L
N–NH3[24]≤1.0 mg N/L
N–NO2[25]≤0.1 mg N/L
N–NO3[26]≤2.0 mg N/L
P–PO4[27]≤0.5 mg P/L
TSS[28]≤25.0 mg/L
TDS[29]≤1000 mg/L
pH[30]6.5–8.5 units
Table 3. Ecological water quality class and their descriptive characteristics [21].
Table 3. Ecological water quality class and their descriptive characteristics [21].
ClassClassificationDescription
ICleanWater contains negligible levels of pollutants
IIModerately pollutedMinor anthropogenic influence
IIIPollutedExceedances of individual parameters are observed
IVDirtyPersistent exceedances of normative values
VVery dirtySignificant exceedances for most parameters
Table 4. Metrics used to assess the importance of NN nodes.
Table 4. Metrics used to assess the importance of NN nodes.
MetricSymbolInterpretation
DegreeD(v)Number of direct links of a node, indicating its local activity
Weighted DegreeDw(v)Accounts for the strength of connections
BetweennessBC(v)Extent to which a node acts as a mediator between other nodes
ClosenessCC(v)Reflects how close a node is to all other nodes in the network
Table 5. Results for the chemical parameters of water quality (2024).
Table 5. Results for the chemical parameters of water quality (2024).
CPM±SDMin.Max.
BOD5, mg O2/L2.6971.5280.7503.050
COD, mgO/L35.33314.97520.00059.000
DO, mg O2/L10.2071.9327.00012.610
N–NH3, mgN/L0.4510.3730.2800.350
N–NO2, mgN/L0.0190.0080.0060.031
N–NO3, mgN/L1.040.550.512.20
P–PO4, mgP/L0.330.170.090.58
TSS, mg/L9.6181.5626.96011.830
pH, units8.3360.1298.1308.550
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Biedunkova, O.; Kuznietsov, P.; Korbutiak, V.; Petruk, A.; Gabrielyan, B.; Andreji, J.; Grokhovska, Y.; Konontsev, S. Dominant Meristic Traits of Fish and Their Association with Habitat Water Quality Parameters: A Case Study. Fishes 2025, 10, 561. https://doi.org/10.3390/fishes10110561

AMA Style

Biedunkova O, Kuznietsov P, Korbutiak V, Petruk A, Gabrielyan B, Andreji J, Grokhovska Y, Konontsev S. Dominant Meristic Traits of Fish and Their Association with Habitat Water Quality Parameters: A Case Study. Fishes. 2025; 10(11):561. https://doi.org/10.3390/fishes10110561

Chicago/Turabian Style

Biedunkova, Olha, Pavlo Kuznietsov, Vasyl Korbutiak, Alina Petruk, Bardukh Gabrielyan, Jaroslav Andreji, Yulia Grokhovska, and Serhii Konontsev. 2025. "Dominant Meristic Traits of Fish and Their Association with Habitat Water Quality Parameters: A Case Study" Fishes 10, no. 11: 561. https://doi.org/10.3390/fishes10110561

APA Style

Biedunkova, O., Kuznietsov, P., Korbutiak, V., Petruk, A., Gabrielyan, B., Andreji, J., Grokhovska, Y., & Konontsev, S. (2025). Dominant Meristic Traits of Fish and Their Association with Habitat Water Quality Parameters: A Case Study. Fishes, 10(11), 561. https://doi.org/10.3390/fishes10110561

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